DEV Community: Hadil Ben Abdallah

Mozaik Hackathon 2026: Build Concurrent Multi-Agent Systems and Compete for $1,000 in Cash Prizes

Hadil Ben Abdallah — Mon, 31 Aug 2026 09:09:32 +0000

Building a multi-agent system sounds simple on a whiteboard. Give one agent a task, let another handle the next step, add a reviewer, connect a few tools, and you have an agentic workflow.

It gets more complicated when those agents need to operate at the same time.

A sequential workflow can force agents into a fixed order: one finishes, another starts, and everyone downstream waits. That model is easy to reason about, but it can become restrictive as the system grows and agents need to react to new information independently.

Mozaik takes a different architectural approach. It is an open-source TypeScript framework for building reactive agents inside an event-driven environment, where agents can work concurrently, respond to events, and coordinate without requiring a central workflow to define every interaction.

And now there is a practical way to try this architecture.

JigJoy, together with daily.dev and Hyperskill, is organizing the Mozaik Hackathon 2026, a free online hackathon focused on building concurrent AI agents.

TL;DR

Building more agents doesn't automatically make a multi-agent system better. The way those agents communicate, react, and depend on one another can have a bigger impact on how the system behaves as it grows.

Mozaik approaches this problem with an event-driven architecture designed around reactive, non-blocking agents. Agents join a shared AgenticEnvironment, receive events, and decide how to react to them.

Here’s what makes the Mozaik Hackathon 2026 worth a look:

Concurrent AI agents: Multiple agents can work at the same time and react to events as they arrive.
Event-driven architecture: Agents, humans, observers, and tools participate in the same AgenticEnvironment.
Non-blocking execution: Inference and message delivery can continue in the background without holding up other participants.
Loosely coupled agents: Agents can operate more independently, making them easier to reuse across projects and applications.
TypeScript-based: Mozaik is an open-source TypeScript framework available through @mozaik-ai/core.
Hands-on learning: Participants build a multi-agent system while exploring agent loops, reasoning-model context, and loop engineering.
$1,000 cash prize pool: $500 for first place, $300 for second, and $200 for third.
Free and online: Developers can participate from anywhere without paying an entry fee.

The hackathon takes place on September 5–6, 2026, with a livestream kickoff on September 4 and winners announced on September 13.

If you've been experimenting with AI agents, this is a chance to move past simple API orchestration and explore how concurrent multi-agent systems can be designed.

Sequential vs. Concurrent Multi-Agent Systems: What Changes With Mozaik?
How Mozaik’s Event-Driven Architecture Enables Concurrent AI Agents
- AgenticEnvironment: A Shared Event-Driven Layer
- Non-Blocking Inference Keeps Agents Moving
- Participants Keep Agents Loosely Coupled
Getting Started With Mozaik: A TypeScript Runtime for Concurrent Agents
Mozaik Hackathon 2026: Build a Concurrent Multi-Agent System
- Who can participate in the Hackathon?
- Mozaik Hackathon Timeline: September 4–13, 2026
What Can You Build With Mozaik?
Why Developers Should Join the Mozaik Hackathon
- You can learn by building alongside other developers
- There is a real incentive, too
Frequently Asked Questions
Final Thoughts

Sequential vs. Concurrent Multi-Agent Systems: What Changes With Mozaik?

When developers first build a multi-agent system, a sequential workflow is often the easiest model to understand.

Imagine a research application with five specialized agents:

A researcher collects information.
An analyst examines the findings.
A critic looks for weaknesses.
A writer turns the results into an answer.
A reviewer checks the final output.

The workflow might look like this:

Researcher
    ↓
Analyst
    ↓
Critic
    ↓
Writer
    ↓
Reviewer

Each agent has a clear responsibility, and each stage can pass its output to the next one. For tasks where every step depends on the previous result, this approach makes sense.

The problem appears when agents don't need to wait for each other.

Suppose the researcher finds three useful sources. The analyst could begin examining the first source while the researcher continues collecting information. A critic could inspect an early finding while the rest of the research is still underway. An observer could monitor the work continuously and react if something looks wrong.

A fixed sequence makes those interactions harder to express because the workflow is built around who runs next, not around which agent should react when something happens.

That distinction is central to Mozaik.

Sequential multi-agent workflow	Mozaik's concurrent model
Agents follow an ordered execution path	Agents react to events as they arrive
One stage commonly waits for another	Multiple agents can work concurrently
Orchestration logic defines the workflow	Participants define their own reactions
Adding an agent can require changes to the workflow	New participants can join the shared environment
Long-running work can hold up downstream stages	Non-blocking inference allows other activity to continue
Agents can become tightly connected to a specific workflow	Agents can operate more independently

The goal isn't to make every AI workflow concurrent. Some tasks really have dependencies that require an order.

Mozaik is useful for the cases where multiple agents need to observe the same activity, react independently, and continue working while other agents are still processing.

That changes how you design the system.

Instead of starting with a chain such as:

Agent A → Agent B → Agent C

you can think about the system as a shared environment in which several participants respond to events:

                    ┌── Research Agent
                    │
                    ├── Planning Agent
                    │
Event ──────────────┼── Coding Agent
                    │
                    ├── Review Agent
                    │
                    └── Observer

The agents still have different responsibilities, but their relationship doesn't have to be encoded as one rigid pipeline.

This also affects agent independence and reuse. When an agent's behavior is based on the events it receives and the handlers it implements, that agent can be easier to move into another application or combine with a different set of participants.

For example, a review agent could be used to evaluate generated code in one application and research findings in another. The surrounding participants can change without requiring the reviewer to become part of a completely different orchestration chain.

That is an important part of Mozaik's approach: concurrency and interoperability are connected to the architecture itself.

The framework gives agents a shared environment where they can collaborate while allowing their individual behavior to remain separate.

And that leads to the next question:

how does Mozaik make this possible under the hood?

How Mozaik’s Event-Driven Architecture Enables Concurrent AI Agents

Mozaik builds its concurrent multi-agent architecture around an AgenticEnvironment.

The environment is shared by humans, agents, observers, and tools. Each participant can emit events, while other participants can listen for the events relevant to their role and decide how to react.

Those events can include plain-text messages, typed ContextItems representing model interactions, and streaming SemanticEvent<T> chunks produced during inference.

The key architectural decision is that participants don't have to wait for a central scheduler to tell them what happens next.

They join the environment, register the handlers they care about, and react when relevant events arrive.

a name="agenticenvironment-a-shared-event-driven-layer">

`AgenticEnvironment`: A Shared Event-Driven Layer

The AgenticEnvironment acts as the communication layer between participants.

A human can send a message into the environment. An agent can receive it through onMessage(). The agent can then start inference, which can produce reasoning, model messages, or function calls. Other participants can observe those events and decide whether they need to respond.

The flow looks more like this:

Message
   ↓
Agent reacts
   ↓
runInference()
   ↓
Model events
   ├── Reasoning
   ├── Function call
   └── Model message
          ↓
    Other participants react

There is no requirement that every participant complete its work before the environment can continue processing other events.

That matters because model inference is not instantaneous. A slow model call should not turn the entire multi-agent system into a waiting line.

Non-Blocking Inference Keeps Agents Moving

Mozaik's runInference() capability is designed to be non-blocking.

A reactive agent can receive a message, add it to its ModelContext, and start inference:

async onMessage(message: string): Promise<void> {
  this.context.addContextItem(
    UserMessageItem.create(message)
  );

  runInference({
    model: "gpt-5.5",
    context: this.context,
    caller: this,
    environment: this.environment
  });
}

The important detail is what happens after runInference() is called: the handler returns while the model continues running.

The agent doesn't have to stay waiting for the inference result before the environment can handle other activity. The same participant can respond to another event, while other participants can continue processing their own events.

When inference produces new ContextItems, those items are sent back through the environment. The agent can react through handlers such as onReasoning, onFunctionCall, and onModelMessage, while other participants can observe corresponding external events.

So the underlying pattern becomes:

Event
  ↓
Reaction
  ↓
Inference or tool call
  ↓
New event
  ↓
Another reaction

Here, Mozaik's reactive agent architecture differs from a workflow that simply executes one function after another. The system can keep responding as new information appears.

Participants Keep Agents Loosely Coupled

Mozaik also separates the participants from the overall application flow.

The base BaseParticipant class provides the foundation for participants, while handlers such as onMessage, onFunctionCall, onReasoning, and onModelMessage allow each participant to implement only the behavior it needs.

That means a participant doesn't have to know the entire application.

A planner can focus on planning.
A researcher can focus on research.
A critic can focus on evaluation.
An observer can monitor events.

They can all participate in the same AgenticEnvironment without requiring one central controller to contain every interaction between them.

This is also what makes the architecture useful for reusable AI agents. If an agent's behavior is defined around the events it understands and the actions it can perform, the same participant can potentially be introduced into another environment with a different combination of agents.

Adding a critic, observer, or specialist can therefore become a matter of composing participants and defining their reactions, instead of redesigning one large workflow every time the system changes.

For developers building AI agent orchestration systems, that is a meaningful change in how the architecture can be structured: the environment provides the shared communication layer, while each participant owns its own behavior.

And this is the kind of architecture the Mozaik Hackathon gives developers a chance to build themselves.

Getting Started With Mozaik: A TypeScript Runtime for Concurrent Agents

Mozaik is written in TypeScript and is available as the @mozaik-ai/core package.

For a new project, installation starts with:

npm install @mozaik-ai/core

You can also install it with Yarn or pnpm:

yarn add @mozaik-ai/core

pnpm add @mozaik-ai/core

The framework resolves the model provider from the model name passed to runInference(). Provider credentials are configured through environment variables, such as:

OPENAI_API_KEY=your-openai-key-here
ANTHROPIC_API_KEY=your-anthropic-key-here
GEMINI_API_KEY=your-gemini-key-here

DeepSeek models can use the OpenAI-compatible chat-completions endpoint with OPENAI_API_KEY and OPENAI_BASE_URL configured for DeepSeek.

For TypeScript projects, Mozaik's documentation recommends a modern moduleResolution setting, such as bundler, node16, or nodenext so package imports resolve cleanly.

Mozaik Hackathon 2026: Build a Concurrent Multi-Agent System

The Mozaik Hackathon is about giving developers the freedom to try out new things with AI agents that work together. It's a chance for them to build something real using Mozaik and see what they can create.

It is fully online and free to enter, and you do not need previous Mozaik experience.

When you sign up, you get a bunch of helpful stuff like documents to read, a template to get started, and a quick intro to get you going before everything kicks off. Plus, the organizers will guide you through the process, so the weekend is not simply a contest where you are handed a framework and left alone with it.

The goal is also educational. You are expected to build something, but the process gives you a chance to understand what is happening underneath an AI agent system: how agents receive events, maintain context, call models and tools, react to outputs, and coordinate with other participants.

Who can participate in the Hackathon?

The hackathon is open to developers anywhere in the world.

You can enter on your own, and the organizers will help solo participants find teammates. You can also bring your own team. The website currently says that team-size limits will be announced soon.

You also do not need to arrive as a Mozaik expert. The event is designed to introduce the runtime to participants before the build weekend begins.

That makes the hackathon relevant to developers who already build AI applications as well as those who are just starting to explore AI agents and multi-agent architecture.

Mozaik Hackathon Timeline: September 4–13, 2026

The main build happens over the weekend of September 5–6, 2026, but the event starts with an introduction on September 4 and ends with the winners' announcement on September 13.

Here is the timeline currently provided by the organizers:

Date	Event	What happens
September 4, 2026	Livestream kickoff	Introduction to Mozaik and the public release of the hackathon brief
September 5–6, 2026	Build weekend	Participants build their concurrent multi-agent systems with Mozaik
September 6, 2026	Submissions close	Submit your repository and short demo by the evening
September 13, 2026	Winners announced	The judging period ends and the winners are announced

You do not need to travel anywhere, find a physical venue, or rearrange your weekend around an in-person event. You can build from wherever you are and communicate with the organizers and other participants through the event's online channels.

What Can You Build With Mozaik?

Once you understand the event-driven model, the interesting part starts: deciding what you want your multi-agent system to do.

The Mozaik Hackathon does not give developers a long list of predefined tracks. There is one open brief, and the core requirement is simple:

build a working system where several agents run at the same time, share state, and coordinate with one another.

That leaves plenty of ideas for creativity.

You could build a research system where multiple agents investigate different parts of a problem simultaneously, with one agent checking the findings as they arrive. You could create a coding team where a planner, implementation agent, tester, and reviewer respond to changes as the project develops.

You could also go beyond familiar developer workflows. Think about customer-support agents that monitor conversations together, autonomous research teams that exchange findings, content systems where writers and fact-checkers react to new information, or monitoring agents that watch another agent's activity and step in when something needs attention.

A project with five agents that only execute one after another won't demonstrate the concurrent-agent architecture as clearly. A smaller system with three agents that genuinely react to shared events and influence each other's work can demonstrate the architecture much better.

The best way to approach the project is to define these three things before writing the code:

What is the shared goal?
Give all participants a reason to collaborate.
What can each agent observe and react to?
This is where Mozaik's event-driven model becomes important.
What happens when agents work at the same time?
Your architecture should make concurrency visible in the actual behavior of the application.

That last point matters because the hackathon is specifically looking for systems where concurrency is genuine, not a sequential pipeline presented as a multi-agent application.

Why Developers Should Join the Mozaik Hackathon

Building a multi-agent system from scratch forces you to understand things that can easily stay hidden when you work with higher-level abstractions.

You have to think about agent state, events, context, model inference, tool calls, communication, reactions, and concurrency. You start seeing an AI agent as an actual software component with inputs, behavior, state, and outputs.

That is exactly the kind of experience the Mozaik team wants participants to gain.

You can learn by building alongside other developers

Hackathons are also useful because the learning does not happen in isolation.

The organizers plan to support participants through Discord, including announcements, team formation for people entering solo, and a place to ask questions throughout the weekend.

If you run into a problem with your architecture, need clarification about Mozaik, or simply want to discuss an approach with other builders, there is a shared space for it.

And because the event is open-ended, you are not limited to reproducing one official demo. You get to make architectural decisions yourself and see what happens when you apply the concurrent-agent model to a problem you care about.

There is a real incentive, too

The hackathon offers $1,000 in cash prizes:

Place	Cash prize
🥇 1st	$500
🥈 2nd	$300
🥉 3rd	$200

There are also additional prizes and discounts shown on the event page, including subscriptions from the event partners.

But for developers interested in AI engineering, the bigger prize is the opportunity to leave the weekend with a working multi-agent application and a clear understanding of how concurrent agents can be designed.

That is a useful project to have in your portfolio, especially as AI applications move beyond single-agent interactions toward systems where several specialized agents collaborate.

Frequently Asked Questions

What makes Mozaik different from other AI agent frameworks?

→ Mozaik is built around a concurrent, event-driven architecture where agents don't have to wait for one another in a fixed sequence. Agents can react to events independently, allowing multiple participants to work at the same time while remaining loosely coupled and reusable across different projects.

Do I need experience with Mozaik to join the hackathon?

→ No. The Mozaik hackathon is open to developers without prior Mozaik experience, and participants receive documentation, a starter template, and a primer before the event. The hackathon is also free and fully online, so you can participate without paying an entry fee or traveling.

What will I build during Mozaik hackathon?

→ You'll build a working multi-agent system around an open brief, with the core requirement that multiple agents genuinely run concurrently and coordinate with one another. Possible directions include a research swarm, a self-reviewing codebase, a live operations room, or a system for parallel hypothesis testing.

When does the Mozaik hackathon take place, and is it free?

→ The Mozaik hackathon takes place online on September 5–6, 2026, and it is free to enter. The livestream kickoff is scheduled for September 4, while submissions close on the evening of September 6.

What prizes can Mozaik hackathon participants win?

→ The hackathon offers $1,000 in cash prizes: $500 for first place, $300 for second place, and $200 for third place. Additional prizes include daily.dev Plus subscriptions, Hyperskill Premium subscriptions, and Mozaik Cloud Premium subscriptions, while every participant receives discounts on Mozaik Cloud and Hyperskill Bootcamps.

Final Thoughts

A lot of today's AI agent development still revolves around deciding what happens first, what happens next, and which agent receives the previous agent's output.

That approach works for many tasks. But as systems become more autonomous, there is another way to think about coordination:

Give agents an environment where they can observe events, react independently, and collaborate as the situation changes.

That is the idea Mozaik is bringing to multi-agent development.

Its event-driven architecture, non-blocking inference model, participant system, and shared environment give developers a foundation for experimenting with agents that can work concurrently without every interaction being hard-coded into one sequential workflow.

The Mozaik Hackathon is a chance to take that idea out of the documentation and build something with it.

You do not need to arrive with a finished architecture or years of multi-agent experience. You need a problem worth solving, a willingness to experiment, and an idea for how multiple agents can contribute to the same goal.

If you have been curious about what happens when AI agents can work together without waiting for each other at every step, September 5–6 is a good weekend to find out.

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Best Enterprise MCP Gateway for Security & Governance in 2026: A Practical Guide to Securing AI Agent Tool Access

Hadil Ben Abdallah — Fri, 28 Aug 2026 08:46:05 +0000

MCP is changing how AI applications interact with the systems around them.

An AI agent can now do much more than generate text. It can connect to MCP servers, discover available tools, query internal systems, interact with databases, and trigger actions on behalf of a user.

That capability is powerful, but it creates a new challenge for engineering teams: how do you control what AI agents are allowed to access, how those actions are tracked, and how much they can consume?

As MCP adoption grows, managing each connection independently quickly becomes difficult. Authentication, permissions, tool access, logging, budgets, and security policies can end up scattered across applications and teams.

This is where an enterprise MCP gateway becomes valuable.

A centralized MCP gateway gives engineering and platform teams a consistent control layer between AI applications and the tools they use. It can enforce policies, manage access, provide visibility into requests, and make MCP infrastructure easier to operate at scale.

In this guide, we'll look at what an enterprise MCP gateway should provide for security and governance, how Bifrost approaches MCP governance, and how Bifrost Edge extends those controls to developer endpoints.

TL;DR

An enterprise MCP gateway provides a centralized layer for controlling how AI applications and agents access MCP servers and the tools they expose.

For production environments, the important capabilities go beyond basic authentication. Engineering teams need:

Centralized authentication and authorization
Tool-level access control
Guardrails and policy enforcement
Audit logs and observability
Usage and cost controls
Consistent governance across AI applications

Bifrost approaches MCP governance through a centralized AI gateway that manages access to models and MCP servers while providing virtual keys, policies, budgets, logging, and observability.

Bifrost Edge extends that governance toward developer endpoints, helping organizations apply the same governance model across the AI applications and developer workflows used by their teams.

The goal isn't to prevent engineers from using AI. It's to give them a controlled infrastructure layer that lets them use increasingly capable AI systems without losing visibility or operational control.

What Is an Enterprise MCP Gateway?

An enterprise MCP gateway is a centralized layer that manages communication between AI applications or agents and the MCP servers they use.

Without a gateway, an AI application can connect directly to multiple MCP servers:

AI Application
      │
      ├── MCP Server A
      ├── MCP Server B
      ├── MCP Server C
      └── Internal Tools

As the number of applications and MCP servers grows, this architecture becomes increasingly difficult to govern.

Different applications may implement authentication differently. Permissions can become inconsistent. Logging may be distributed across several systems, and platform teams may have little visibility into which applications are accessing sensitive tools.

A gateway introduces a centralized control layer:

AI Applications
      │
      ▼
Enterprise MCP Gateway
      │
      ├───────────────┬───────────────┐
      ▼               ▼               ▼
   MCP Server A    MCP Server B    MCP Server C
      │               │               │
      ├── Tools       ├── Tools       ├── Tools
      ├── APIs        └── Databases   └── Internal Systems
      └── Services

The gateway becomes the place where organizations can establish consistent rules before requests reach MCP servers.

For enterprise environments, this matters because MCP servers can expose capabilities that go far beyond retrieving information. Depending on the tools available, an agent could interact with internal databases, APIs, cloud infrastructure, or other systems.

The more capable those tools become, the more important centralized governance becomes.

MCP Gateway vs. Direct MCP Connections

Direct connections can work well when experimenting with MCP or building a small application.

An individual developer might connect an AI client to a handful of MCP servers and manage the configuration locally. At that scale, the setup is relatively easy to understand.

The situation changes when MCP becomes part of an organization's engineering infrastructure.

Imagine several teams using different AI applications, each connecting to different MCP servers. Some applications need access to internal documentation, while others require access to development tools or production systems.

Now the organization needs to answer questions such as:

Which applications can access each MCP server?
Which users can invoke sensitive tools?
What happened during a specific tool call?
Which teams are generating the most usage?
Can access policies be changed centrally?

A direct-connection architecture makes those questions harder to answer.

A gateway provides a shared governance layer where these decisions can be managed consistently.

Enterprise MCP Gateway vs. MCP Proxy

An MCP proxy can forward requests between an AI client and an MCP server.

An enterprise MCP gateway goes further by making that connection a governed part of the organization's AI infrastructure.

The distinction becomes important when teams need more than connectivity.

An enterprise gateway can bring together authentication, authorization, tool access, policies, logging, observability, and cost controls around MCP traffic.

That makes the gateway useful not only for connecting AI applications to MCP servers, but also for governing how those connections are used.

Why MCP Security Has Become an Enterprise Problem

MCP introduces a different security challenge because AI agents can interact with tools on a user's behalf.

An MCP server might expose several capabilities through a single connection. Some tools may simply retrieve information, while others can interact with systems that contain sensitive data or perform actions with real consequences.

As organizations add more MCP servers, the number of possible agent-to-tool interactions grows quickly.

A developer might use one MCP server for documentation, another for databases, and another for cloud infrastructure. A different team may use an entirely different set of tools.

The challenge isn't determining whether someone is allowed to use MCP.

The real question is:

What exactly should this application, agent, or user be allowed to do through MCP?

That distinction is at the heart of enterprise MCP governance.

Why MCP Security Requires More Than Authentication

Authentication answers an important question:

Who is making the request?

But enterprise MCP security also needs to answer:

What is that identity allowed to do?

An engineer may be authorized to use an AI assistant, for example, but that doesn't necessarily mean the assistant should have access to every MCP tool available across the organization.

Similarly, an internal application may access company documentation without needing permission to interact with production databases.

These are authorization and governance decisions.

That's why authentication alone isn't enough for enterprise MCP deployments. Organizations need a way to define and enforce policies around the tools and systems AI agents can access.

Without centralized controls, each application may end up implementing those decisions independently.

That creates the same governance problem that appears when AI integrations are scattered across different applications: permissions become inconsistent, visibility becomes fragmented, and security teams have a harder time understanding what is happening across the environment.

What Should an Enterprise MCP Gateway Provide?

A useful enterprise MCP gateway needs to do more than route requests.

For production AI environments, the gateway should provide a combination of access control, policy enforcement, observability, and governance.

The goal is to create one consistent layer where organizations can control how AI applications interact with MCP infrastructure.

MCP Authentication and Authorization

Authentication provides the foundation for controlling MCP access.

Bifrost uses Virtual Keys to separate applications from the underlying provider credentials and define how they can interact with AI infrastructure.

Instead of giving every application direct access to provider credentials, applications authenticate through Bifrost while the gateway handles the underlying connections.

Virtual keys can be used to define policies around:

Which models an application can use
Which providers are available
Spending limits
Rate limits
Allowed MCP servers
Tool permissions
Logging behavior

This creates a clear separation between an application's identity and the credentials used behind the gateway.

For enterprise MCP deployments, that same principle is valuable because access can be governed centrally instead of being embedded independently into every AI application.

MCP Tool Filtering and Access Control

MCP servers can expose multiple tools, and not every application should necessarily have access to all of them.

Consider an MCP server connected to internal infrastructure.

One tool might retrieve documentation. Another might query a database. A third could potentially perform an operational action.

Treating all three capabilities as equivalent creates unnecessary risk.

Bifrost extends governance to the MCP tool layer, allowing organizations to control which MCP servers and tools can be accessed through the gateway.

This gives platform teams a more precise way to define AI permissions.

The question becomes about which capabilities that application is actually allowed to use.

Audit Logs and MCP Observability

Access control answers what an AI application can do.

Audit logs help answer what it actually did.

This becomes especially important when AI agents interact with multiple tools and systems.

Without centralized logging, investigating an unexpected action can mean searching through application logs, provider dashboards, MCP server logs, and other monitoring systems.

Bifrost provides centralized visibility into AI requests passing through the gateway.

Depending on the workflow, teams can track information such as:

The application initiating the request
The virtual key being used
The user or service involved
The model handling the request
MCP tools being accessed
Latency
Token usage
Associated costs

That creates a more complete picture of the AI request lifecycle.

For engineering teams, this isn't only useful during security investigations. The same visibility can help with debugging, performance analysis, optimization, and compliance reviews.

Guardrails and Policy Enforcement

Authentication and tool permissions establish who can access an MCP environment, but enterprise governance also needs rules that apply consistently to every request.

This is where guardrails become important.

Without centralized policies, individual applications have to decide how requests should be handled. One application may restrict certain models or tools, while another may allow them. Over time, those differences create gaps that are difficult for platform teams to monitor.

Bifrost provides a centralized layer where organizations can apply policies across their AI infrastructure.

Depending on the environment, those policies can include:

Restricting access to specific models
Limiting which providers can be used
Blocking sensitive MCP tools
Enforcing role-based permissions
Applying rate limits
Separating development and production environments

The advantage is consistency.

Developers don't have to implement the same governance rules separately in every application they build. The gateway can enforce the organization's policies while developers continue working with the AI tools and models they need.

For enterprise MCP deployments, this becomes especially important as the number of agents and tools grows. A policy that works for five applications should not require a completely different implementation when the organization reaches fifty.

AI Governance and Cost Control

Security isn't the only governance problem that appears when AI usage scales.

Cost control matters too.

MCP-enabled agents can interact with multiple models and tools during a single workflow. When usage is spread across different applications, teams, and providers, understanding where AI spending comes from becomes increasingly difficult.

One team might use one model heavily for development. Another might rely on a different provider for production workloads. Meanwhile, AI agents can generate additional requests as they interact with MCP tools.

Without centralized visibility, organizations can end up discovering unexpected spending only after the bill arrives.

Bifrost brings usage and budget controls into the same infrastructure layer that handles AI requests.

Organizations can define controls such as:

Usage limits
Spending caps
Rate limits
Team-specific budgets
Project-specific allocations

Because requests pass through the gateway, teams can connect usage with the applications, models, and workflows generating that activity.

That changes the conversation around AI costs.

Instead of simply asking why the monthly bill increased, platform teams can investigate which projects generated the usage, which models were involved, and where the costs came from.

Good AI governance isn't only about blocking risky behavior. It's also about making AI usage visible, predictable, and manageable.

How Bifrost Approaches Enterprise MCP Governance

Bifrost approaches MCP governance as part of a broader AI gateway architecture.

The idea is straightforward: AI applications shouldn't have to independently manage every model connection, MCP server, credential, permission, budget, and logging requirement.

A centralized gateway can handle those concerns consistently.

With Bifrost, the gateway provides a control layer for AI infrastructure that includes:

Virtual Keys for managing application access
MCP governance for controlling access to MCP servers and tools
Guardrails for enforcing centralized policies
Budgets and rate limits for controlling usage
Audit logs and observability for understanding AI activity
LLM routing across multiple providers

This architecture is useful because MCP doesn't exist in isolation.

An AI application may use an LLM for reasoning, call an MCP server for additional context, access internal systems through tools, and continue the workflow through several subsequent model requests.

Trying to govern each part independently can quickly become complicated.

A gateway provides one place where these interactions can be managed.

For example, a simplified workflow might look like this:

AI Application / Agent
          │
          ▼
   Bifrost Gateway
          │
    ┌─────┴─────┐
    │           │
    ▼           ▼
LLM Providers  MCP Servers
    │           │
    │       ┌───┴────┐
    │       ▼        ▼
    │    Tool A    Tool B
    │
    └──── Governance ────┐
                         │
          Keys • Policies • Budgets
          Logs • Access • Observability

The important part isn't simply putting another component in the request path.

It's creating a consistent governance boundary around AI infrastructure.

That gives platform teams a central place to establish policies while application teams can focus on building AI-powered products and workflows.

How Bifrost Edge Extends MCP Governance to Developer Endpoints

A gateway provides centralized governance for requests that pass through it.

But modern developers don't interact with AI through only backend applications.

They use coding agents, desktop AI applications, browser-based assistants, IDE integrations, and MCP-enabled developer tools directly from their machines.

That creates another governance challenge.

A company may have carefully configured its gateway, but if developers can use AI applications that bypass the organization's intended infrastructure, those policies may not cover every AI interaction.

Bifrost Edge extends the governance model toward the endpoint.

The goal is to bring AI traffic from developer environments into the same centralized governance architecture.

A developer might use several different AI tools during a normal workday:

Coding agents
Desktop AI assistants
Browser AI applications
IDE integrations
MCP-enabled tools
Terminal workflows

Without an endpoint layer, each application can become another place where AI access and configuration need to be managed.

Bifrost Edge helps extend the governance established through Bifrost Gateway to those developer workflows.

That creates a broader architecture:

Developer Endpoints
       │
       ▼
   Bifrost Edge
       │
       ▼
 Bifrost Gateway
       │
   ┌───┴──────────┐
   ▼              ▼
LLM Providers  MCP Servers

The gateway remains the central governance layer, while Edge helps bring developer-side AI activity into that controlled environment.

What Enterprise MCP Governance Looks Like in Practice

Consider an engineering organization where different teams use different AI tools.

Backend engineers might use coding agents. Frontend developers may work with AI inside their IDEs. Other teams may use desktop assistants or browser-based AI tools.

At the same time, internal agents may connect to MCP servers exposing documentation, databases, APIs, or other enterprise resources.

Without centralized governance, each workflow can develop its own permissions, credentials, and logging.

With Bifrost Gateway and Edge, the organization can establish a more consistent path:

Developer / AI Application
           │
           ▼
      Bifrost Edge
           │
           ▼
     Bifrost Gateway
           │
     ┌─────┴─────┐
     ▼           ▼
  LLMs       MCP Servers
     │           │
     └─────┬─────┘
           ▼
   Centralized Governance

The same core principles can then apply across the workflow: identity, permissions, tool access, guardrails, budgets, logging, and observability.

That consistency becomes valuable as organizations move from experimenting with MCP to operating AI agents as part of everyday engineering workflows.

What to Look for in the Best Enterprise MCP Gateway

Choosing an enterprise MCP gateway shouldn't come down to whether it can simply connect an AI application to an MCP server.

The more important question is whether it gives engineering teams enough control to operate those connections safely at scale.

Here are the capabilities worth evaluating.

1. Centralized authentication and authorization

The gateway should provide a consistent way to identify applications and control what they can access.

2. Tool-level access control

MCP servers can expose many capabilities. Organizations should be able to control which tools different applications, users, or workflows can access.

3. Centralized audit logs

Teams need visibility into AI requests and tool activity when debugging incidents, investigating unexpected behavior, or reviewing compliance requirements.

4. Policy enforcement

Security and governance policies should live in a centralized layer so applications don't have to recreate the same controls independently.

5. Budget and usage controls

As AI adoption grows, organizations need to understand and manage consumption across models, applications, and teams.

6. LLM and MCP support in one architecture

MCP governance becomes more useful when it is connected to the rest of the AI infrastructure instead of being treated as an isolated feature.

7. Endpoint governance

For organizations with developer-facing AI workflows, governance should also account for AI applications running directly on employee machines.

These capabilities turn an MCP gateway from a simple connectivity layer into infrastructure that platform and security teams can operate.

How to Choose the Best Enterprise MCP Gateway in 2026

There isn't one universal MCP gateway that is perfect for every organization.

The right choice depends on how your teams use AI, how many MCP servers you operate, what systems those tools can access, and how much centralized governance you need.

A useful evaluation should start with a few practical questions:

How many AI applications and agents need MCP access?

A small experiment may not require a full governance layer. A large engineering organization with dozens of AI workflows has very different requirements.

How sensitive are the tools behind your MCP servers?

If MCP connects agents to internal databases, production infrastructure, or business-critical systems, granular access controls become much more important.

Can policies be managed centrally?

If every application requires separate configuration, governance becomes harder to maintain as adoption grows.

Can you trace AI activity?

Audit logs should make it possible to understand which application, user, model, or tool was involved in an AI workflow.

Can you control AI spending?

As organizations adopt multiple models and AI agents, cost visibility becomes part of infrastructure management.

Does governance extend to developer endpoints?

If employees use coding agents, desktop assistants, or other AI applications directly, endpoint coverage can be just as important as server-side governance.

For many engineering teams, the best enterprise MCP gateway will be the one that fits naturally into the broader AI infrastructure.

A Practical Enterprise MCP Governance Checklist

Before adopting an MCP gateway, it's worth turning the requirements into a practical checklist.

An enterprise MCP deployment should give engineering and security teams clear answers to a few basic questions:

Who can access MCP?
What can they access?
What happened?
How much are they using?

A useful checklist includes:

[ ] Centralized authentication and authorization
[ ] Virtual keys or another consistent identity mechanism
[ ] Tool-level access control
[ ] MCP server access policies
[ ] Centralized audit logs
[ ] Request and usage observability
[ ] Guardrails and policy enforcement
[ ] Usage limits and spending controls
[ ] Support for multiple LLM providers
[ ] Governance across developer AI applications and endpoints

The exact requirements will vary between organizations, but the underlying principle remains the same: MCP governance should become easier as the organization grows, not harder.

If adding another MCP server means creating another set of credentials, permissions, dashboards, and policies manually, the architecture will eventually become difficult to maintain.

A centralized gateway helps keep those controls in one place.

Frequently Asked Questions

What is an enterprise MCP gateway?

→ An enterprise MCP gateway is a centralized infrastructure layer that manages and governs communication between AI applications or agents and MCP servers.

It can provide authentication, authorization, tool access control, policy enforcement, audit logs, observability, and usage controls.

Why do enterprises need an MCP gateway?

→ Enterprises often have multiple AI applications, agents, and MCP servers connecting to internal systems.

Managing those connections independently can create inconsistent permissions, fragmented logs, and limited visibility.

An MCP gateway provides a centralized place to manage those interactions and apply consistent governance policies.

How does an MCP gateway secure AI agents?

→ An MCP gateway can control which applications and users can access MCP servers and which tools they are allowed to invoke.

It can also apply policies, enforce limits, and maintain audit logs so teams can understand how AI agents are interacting with enterprise systems.

What is the difference between an MCP gateway and an MCP proxy?

→ An MCP proxy primarily forwards communication between an AI client and an MCP server.

An enterprise MCP gateway adds governance capabilities around that traffic, including authentication, authorization, tool permissions, policies, logging, observability, and cost controls.

Can an enterprise MCP gateway control individual MCP tools?

→ Yes. Tool-level access control is an important part of MCP governance because an MCP server can expose multiple capabilities with different levels of sensitivity.

Controlling individual tools allows organizations to give AI applications only the capabilities they actually need.

Final Thoughts

MCP is making AI applications more capable, but that capability also changes what engineering teams need to control.

Once AI agents can interact with internal tools, databases, APIs, and other systems, simply knowing which model an application uses isn't enough.

Teams need to know who can access those tools, what they can do, how their activity is monitored, and how usage is controlled.

That's the role an enterprise MCP gateway can play.

Bifrost approaches this problem through a centralized AI gateway that brings MCP governance together with authentication, virtual keys, tool access, guardrails, budgets, audit logs, and observability.

Bifrost Edge extends that model toward developer endpoints, helping organizations maintain consistent governance across the AI applications and workflows their teams use.

Bifrost Edge extends that model toward developer endpoints, helping organizations maintain consistent governance across the AI applications and workflows their teams use. If you'd like to explore the architecture in more detail, you can book a demo with the Bifrost team.

As AI agents become more capable, these controls will become important. The organizations that can give their developers powerful AI tools while maintaining clear visibility and control will be in a stronger position to scale AI across engineering teams.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

I Tested 5 Design to Code Tools With the Same Outdated SaaS Dashboard

Hadil Ben Abdallah — Wed, 26 Aug 2026 09:05:06 +0000

A polished UI can make a product feel completely different, but getting there usually takes more than changing a few colors or swapping out an old chart. Modern design to code tools can now handle much of that work, from redesigning an existing interface to generating the frontend code behind it.

That’s what made me curious about today’s design to code tools.

Instead of testing each tool with a different project or judging them from a demo, I wanted to put them on equal ground. So I took the same outdated SaaS analytics dashboard, gave the same dashboard screenshot to five popular tools, and used the same redesign prompt for every test.

The five tools I tested were:

Flowstep
Anima
v0 by Vercel
Lovable
Bolt.new

The goal wasn’t to see which tool could produce the prettiest screenshot.

I wanted to see how each one handled the same real-world UI problem: modernizing an old dashboard while keeping its core information and functionality intact.

I also looked at the generated frontend code, because a beautiful interface doesn’t help much if the code underneath it becomes a mess that developers have to rebuild.

So, how different were the results when every tool started with exactly the same design and instructions?

Let’s find out.

TL;DR

I tested five design to code tools, Flowstep, Anima, v0 by Vercel, Lovable, and Bolt.new, using the same outdated SaaS analytics dashboard and the same redesign prompt.

The original dashboard had the kind of interface you might recognize from older analytics products: dense navigation, dated typography, limited visual hierarchy, tightly packed content, and a layout that could use a serious UX refresh.

The challenge for each tool was to preserve the dashboard’s core information while giving it a modern 2026 interface.

I evaluated the results across several areas, including visual design, hierarchy, spacing, typography, component quality, UX, and frontend code quality.

Here’s the quick result:

Flowstep → Strongest overall redesign, with a modern visual system, clear hierarchy, polished spacing, and well-structured frontend code.
Anima → Produced a cleaner interface with good code, although the final design still felt fairly basic.
v0 by Vercel → Stayed close to the original dashboard while improving its presentation; the result still needed more visual refinement.
Lovable → Delivered a clean, well-designed dashboard with solid frontend code and a good overall balance.
Bolt.new → Created a well-structured interface with good hierarchy and typography, although the result needed some additional updates.

The test also showed something that can get lost in AI tool comparisons: design to code tools are not equally good at the same parts of the workflow.

A tool can produce impressive code while delivering an average visual redesign, while another can create a polished interface but leave more work for the developer.

That’s why I’m not treating this as a competition to discover which tool generated the prettiest UI. What matters more to me is how much each tool improved the starting design and how usable the result would be for a real frontend project.

What Are Design to Code Tools?

Design to code tools are platforms that help turn visual designs, screenshots, UI concepts, or product requirements into frontend interfaces and code.

The category has changed quite a bit with the rise of AI. Traditional design to code workflows often started with a finished design file, such as a Figma project, and focused on translating that design into HTML, CSS, React, or another frontend stack.

Modern AI-powered tools can start much earlier in the process.

You might give a tool a Figma design and ask it to generate the implementation. You might provide a screenshot and ask it to recreate or redesign the interface. Or you might describe the product you want and have the tool generate the UI and application code from a prompt.

That makes design to code tools useful for several different workflows, including:

Figma to code
screenshot to code
AI UI generation
prompt to UI development
AI app building

But there’s an important difference between generating something that looks good and generating something that developers can work with.

A dashboard can look impressive in a screenshot and still have poor component structure, awkward responsive behavior, duplicated styles, or code that needs extensive cleanup. On the other hand, clean code isn’t enough if the generated interface barely improves the original design.

Testing 5 Design to Code Tools With the Same SaaS Dashboard

To make the comparison as fair as possible, I kept the starting point and instructions consistent across all five tests.

Each tool received the same outdated dashboard screenshot and the same prompt. I then evaluated the generated result.

The Outdated SaaS Analytics Dashboard

For the test, I used an older SaaS analytics dashboard with a dense desktop layout and a dated visual style.

The original interface contains several common analytics dashboard elements: a left-hand navigation menu, a top navigation bar, site metrics, a line chart, metric cards, content tables, action statistics, traffic-source charts, visitor information, referrers, and recent activity.

Old SaaS analytics dashboard

The screen works as a good test case because there is plenty of useful information to preserve, but there is also a lot of room for a visual and UX redesign.

The challenge wasn’t to invent a completely different product. The tools needed to understand the existing dashboard, keep its core information and functionality, and give it a more modern interface.

That makes the test closer to a real redesign task than simply asking an AI tool to create a dashboard from scratch.

The Prompt I Gave Every Tool

I used exactly the same prompt for all five tools:

Redesign this outdated SaaS analytics dashboard with a modern 2026 UI.
Keep the same core information, functionality, and content, but improve the visual hierarchy, spacing, typography, and overall user experience.

I deliberately kept the prompt relatively short without too many details to see the creativity of each tool.

The SaaS dashboard screenshot provided the visual context, while the prompt established the main requirements.

How I Compared the Results: Evaluation Criteria

I didn’t judge the five outputs on appearance alone.

For each result, I looked at several aspects of the redesign and implementation:

Visual design: Does the redesigned dashboard actually feel modern and polished?
Visual hierarchy: Are the most important metrics, charts, navigation elements, and actions easy to identify?
Spacing and layout: Does the interface have enough breathing room, or does it still feel crowded?
Typography: Are font sizes, weights, and text hierarchy used effectively?
Component quality: Are cards, charts, tables, navigation, and other UI elements presented as a coherent system?
User experience: Is the dashboard easier to scan, understand, and use than the original?
Code quality: Is the generated frontend code structured, readable, and practical for further development?
Room for improvement: How much additional work would I need to do before considering the result ready for a real project?

The last criterion matters because AI-generated UI is rarely finished after the first prompt.

A useful design to code tool should give you a strong starting point, but the real value comes from how much work remains after that first generation.

If the result looks good but requires a complete rebuild, the time savings are smaller than they appear.

Quick Comparison Table

Before getting into each tool in detail, this comparison table provides a quick overview of the five tools I tested. It compares them based on their ideal use case, free availability, starting price, and standout capabilities, helping designers and developers quickly find the design to code tool that best fits their workflow.

Tool	Best For	Free Plan	Starting Price	Standout Feature
Flowstep	Modern UI redesigns and frontend code	✅	$15/mo	Design to code workflow with editable UI and structured code
Anima	Turning existing designs into frontend code	✅	$25/mo	Figma-to-code workflow
v0 by Vercel	React UI and application generation	✅	$30/mo	React, Tailwind CSS, and shadcn/ui generation
Lovable	Rapid web app prototyping	✅	$25/mo	Full-stack app generation from natural language
Bolt.new	Browser-based AI development	✅	$25/mo	Code, preview, and development environment in one place

The table gives you the quick answer, but it doesn't tell the whole story. Two tools can generate perfectly usable dashboards while taking very different approaches to design and code.

So, let’s look at each tool individually.

1. Flowstep

Flowstep is an AI design engineer that helps turn a product UI idea into editable interface designs and frontend code. It is closer to the design and development workflow than a traditional image-to-code converter, which makes it useful when you want to rethink an existing interface and then continue toward implementation.

What It Generated

Flowstep produced the biggest visual transformation of the five tools I tested.

The redesigned dashboard moved away from the dated layout and introduced a much more modern 2026 interface. The visual hierarchy was noticeably stronger, with clearer separation between the main analytics area, supporting metrics, charts, and secondary information.

The spacing and typography also felt more intentional. Elements had more room to breathe, the different sections were easier to scan, and the dashboard felt closer to something you would expect from a current SaaS analytics product.

The component structure was another strong point. The generated frontend code was clear and well organized, which made the result more useful from a development perspective.

Flowstep's redesigned SaaS analytics dashboard

Key Features

AI-powered UI generation: Create interface designs from natural-language descriptions.
Multi-screen workflows: Generate connected product screens from the same design context.
Editable designs: Continue refining the generated interface after the initial generation.
Frontend code generation: Generate code that can serve as a starting point for implementation.
React and TypeScript support: Useful for modern frontend development workflows.
Tailwind CSS and shadcn/ui: Works well with popular React-based UI technologies.
Figma copy-paste: Move generated designs into Figma without relying on a separate plugin.
Reference-based design: Use visual references and other context to guide the generated interface.
MCP integration: Connect design context with compatible coding agents and development workflows.

Pricing

Flowstep offers a free tier, with paid plans starting at $15/month.

2. Anima

Anima is a design to code platform that helps turn existing designs, websites, and visual concepts into frontend code. Its workflow is especially useful when you already have a design and want to move from the visual interface to an editable implementation that developers can continue working with.

What It Generated

Anima produced a cleaner version of the dashboard, and the overall redesign was definitely an improvement over the original interface.

The layout felt more organized, and the updated visual treatment made the dashboard easier to look at. The core analytics information was still there, so the redesign didn't lose the purpose of the original screen.

That said, compared with some of the other results in this test, the final design still felt fairly basic. It looked cleaner and more modern than the original dashboard, but it didn't go as far visually as I expected from a complete 2026 redesign.

The code was a stronger part of the result. The generated frontend structure was good and provided a reasonable starting point for further development. For teams that already have a design and mainly want to speed up the transition from design to implementation, that makes Anima worth considering.

Anima's redesigned SaaS analytics dashboard

Key Features

Figma-to-code conversion: Convert existing design files into frontend code.
React code generation: Generate React-based implementations from designs.
HTML and CSS export: Support projects that don't use a React stack.
Design-to-development workflow: Reduce manual recreation between design and implementation.
Component-based output: Turn parts of a design into reusable frontend components.
Responsive design support: Translate responsive layouts into code.
AI-assisted workflows: Use AI to help accelerate design and development tasks.
MCP support: Connect design context with compatible AI coding workflows.

Pricing

Anima offers a free plan, with paid plans starting at $25/month.

3. v0 by Vercel

v0 by Vercel is an AI-powered development platform that turns natural-language prompts and visual references into interfaces and web applications. It is especially well suited to React-based projects, with strong support for technologies such as Tailwind CSS and shadcn/ui.

What It Generated

v0 stayed fairly close to the original dashboard I provided.

That isn't necessarily a bad thing. The generated version preserved much of the original information and overall structure, which means the result was easy to recognize as a redesign of the same product rather than an entirely different dashboard.

The visual treatment was cleaner, and the layout had a more contemporary feel, but I still think it needed another round of refinement before I would call it a polished 2026 SaaS dashboard. Some areas could use stronger visual hierarchy, more distinctive component styling, and additional attention to spacing and overall visual balance.

The code was one of the stronger parts of the result. The generated implementation was good and gave me a solid foundation to continue improving the interface. This is where v0 makes a lot of sense for developers who care about getting from an interface concept to editable React code quickly.

v0's redesigned SaaS analytics dashboard

Key Features

Prompt-to-UI generation: Describe an interface or application and generate a working frontend.
React support: Generate React components and applications for modern web development.
Tailwind CSS: Build interfaces using a widely adopted utility-first CSS framework.
shadcn/ui integration: Generate interfaces using reusable, customizable UI components.
Visual iteration: Preview and refine generated interfaces as you work.
Code editing: Developers can inspect and modify the generated implementation.
Application generation: Go beyond individual screens and create more complete web applications.
Vercel integration: Fits naturally into workflows that already use Vercel for deployment and hosting.

Pricing

v0 offers a free plan with usage limits. Paid plans start at $30/month.

4. Lovable

Lovable is an AI app builder that turns natural-language instructions into functional web applications. It goes beyond generating a single interface because the platform can also work with application logic, databases, authentication, and other pieces needed to turn an idea into something users can actually interact with.

What It Generated

Lovable produced a clean and modern redesign that successfully moved the dashboard away from its original dated appearance.

The information was reorganized into a more contemporary dashboard structure, while the interface retained the important analytics content from the original screen. The result felt cleaner without becoming unnecessarily complicated.

The visual hierarchy was also good. The main metrics were easier to identify, supporting information had clearer separation, and the overall dashboard felt more cohesive.

The generated code was another positive point. It was clean enough to provide a useful foundation for continued development, which is important when you're using an AI tool for more than a quick visual prototype.

Lovable's redesigned SaaS analytics dashboard

Key Features

Natural-language app generation: Describe what you want to build and generate a functional application.
AI-powered UI generation: Create and refine interfaces through prompts.
Full-stack development: Build more than static frontend screens.
React-based applications: Generate modern web application interfaces that developers can continue editing.
Database integration: Connect applications to backend data and database services.
Authentication: Add login and user-management functionality.
Conversational editing: Continue modifying the application through natural-language instructions.
Deployment workflows: Move generated applications toward a usable deployed experience.
Code ownership and editing: Continue working with the generated project instead of being limited to a static mockup.

Pricing

Lovable offers a free plan with usage limits. Paid plans start at $25/month.

5. Bolt.new

Bolt.new is an AI-powered development environment from StackBlitz that lets you create, edit, run, and preview web applications directly in the browser. It combines AI-assisted coding with an in-browser development environment, so you can move from an idea or design reference to a working project without setting up the entire development environment locally first.

What It Generated

Bolt produced a well-structured redesign with a clear visual hierarchy.

The dashboard was more organized than the original, and the typography, spacing, and overall arrangement made the information easier to scan. The core analytics content remained recognizable, while the interface received a more contemporary treatment.

The result wasn't completely finished, though. A few areas still needed additional updates before the dashboard felt fully polished. Some components could benefit from more refinement, and another iteration would help bring the visual language together more consistently.

The code was good and provided a useful foundation for continued development. That's an important advantage of Bolt: once the initial interface is generated, you can continue working on the actual project inside the same browser-based environment.

Bolt.new's redesigned SaaS analytics dashboard

Key Features

Prompt-based development: Describe an application or interface and generate code from natural-language instructions.
In-browser coding environment: Write and modify code without setting up a local development environment first.
Live preview: See the application as you build and make changes.
AI-assisted coding: Use prompts to create, modify, and extend application features.
Multiple framework support: Work with technologies such as React, Vue, Svelte, and Astro.
Built-in terminal: Run development commands within the browser environment.
Full application workflows: Build beyond isolated UI components.
Code access: Developers can directly inspect and modify the generated implementation.
Deployment support: Move projects from development toward a live application.

Pricing

Bolt.new offers a free plan with usage limits. Its paid plans start at $25/month.

What I Learned From Testing All 5 Design to Code Tools

After putting all five tools through the same test, I came away with a clear picture of what separates these platforms.

The first lesson is that visual quality and code quality are two different things. A tool can generate a beautiful dashboard and still leave you with code that needs significant cleanup. Another tool can produce very respectable frontend code while the visual result needs another design pass.

That became obvious throughout this test.

There was also a second lesson that I think matters even more: the best design to code tool depends on what you want to do after the first generation.

If you're only judging the screenshots, you might choose a different winner than if you're planning to take the generated code into a real project.

A beautiful first screen is great. But if you need to spend hours rebuilding components, fixing responsiveness, cleaning up the code, and recreating the design system afterward, some of that initial time saving disappears.

Which Design to Code Tool Should You Choose?

I don't think there is one answer that works for everyone. It depends on what you are trying to accomplish with a design to code tool.

If your priority is creating a modern UI from an existing design or visual reference and getting clean frontend code alongside it → Flowstep is the strongest choice from my test. It gave me the biggest visual improvement while also producing frontend code that was clear and structured.

If you're working heavily with Figma designs → Anima makes more sense. Its workflow is built around taking existing designs and moving them toward implementation, and the code it generated in my test was good. The redesign itself just didn't feel as visually ambitious as some of the other results.

If you're a developer who wants React code and a strong component foundation → v0 is worth considering. Its result stayed fairly close to the original dashboard, but the generated code was good, and the interface can be refined further.

If your goal goes beyond the UI and you want to turn an idea into a working application → Lovable is a stronger fit. Its clean dashboard result was only one part of the workflow; the platform can also handle application logic and backend functionality.

And if you want an AI development environment where you can generate, edit, preview, and continue coding in the browser → Bolt.new is a compelling option. Its dashboard had a good structure and hierarchy, although I would still give the design another pass before considering it finished.

Here's how I'd break down the five based on this particular test:

If you want...	My pick
The strongest overall UI redesign + frontend code	Flowstep
Figma-to-code workflows	Anima
React and shadcn/ui development	v0 by Vercel
A functional full-stack application	Lovable
An in-browser AI development environment	Bolt.new

This wasn't a benchmark of everything these products can do. I gave them one outdated SaaS dashboard and one prompt. Your results can change depending on the complexity of the design, the quality of the prompt, the framework you're targeting, and how much iteration you do after the first generation.

So if you're choosing between these design to code tools, I'd use my results as a starting point, then test the tool against a screen from your own product. That's where you'll find out whether it fits your workflow.

FAQs About Design to Code Tools

What are the best design to code tools in 2026?

→ The best design to code tool depends on the type of input you're working with and the output you need. In my test, Flowstep produced the strongest overall redesign and clean frontend code. Anima is a good option for existing design to code workflows, especially when Figma is involved. v0 is well suited to React development, while Lovable and Bolt.new are useful when you want to turn ideas into working web applications.

Can AI design to code tools turn an existing UI into code?

→ Yes. Many modern design to code tools can take an existing visual design, screenshot, or interface and generate frontend code from it.

The exact workflow varies between products. Some tools are built around Figma-to-code conversion, while others can use screenshots or visual references as input. AI app builders can also use an existing interface as context while generating a new implementation.

The generated code should still be reviewed before it goes into production. Check the component structure, responsiveness, accessibility, performance, dependencies, and how well the implementation fits your existing codebase.

Are design to code tools good enough for production?

→ They can provide a strong starting point for production development, but I wouldn't treat the first generated result as production-ready without review.

Production readiness involves more than making a screen look good. Developers still need to consider responsive behavior, accessibility, performance, security, component reuse, state management, error handling, testing, and maintainability.

The real value of these tools is that they can reduce the amount of repetitive work required to reach that stage.

What is the difference between design to code tools and AI app builders?

→ The difference is mainly in the starting point and the scope of the output.

Design to code tools generally focus on moving from a visual design or UI concept toward frontend implementation. They can help translate an existing design into components and code or generate a UI from a description.

AI app builders usually take the process further. They can start with a product idea and generate not only the interface but also application logic, database functionality, authentication, and other parts of a working application.

Final Thoughts

Testing five design to code tools with the exact same dashboard gave me a better understanding of what these tools are good at.

The biggest conclusion is that AI can now do much more than simply turn a static design into a few lines of frontend code. It can interpret an existing interface, make design decisions, reorganize information, generate components, and produce a working implementation in a short amount of time.

But speed doesn't remove the need for judgment.

A generated dashboard still needs someone to decide whether the hierarchy makes sense, whether the interface is accessible, whether the layout works across screen sizes, and whether the code can realistically live inside a larger application. The first generation is only one step in the process.

That's also why I think testing the same project across multiple design to code tools is more useful than looking at feature lists alone. When every tool receives the same input, differences in design quality, code structure, usability, and refinement become easier to see.

And there isn't necessarily one tool that will produce the best result for every project. Your starting design, framework, level of customization, development workflow, and willingness to iterate can all change the outcome.

The important part is knowing where the generated result is good enough, where it needs another iteration, and where a developer still needs to take over.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

I Used React DataGrid to Build a Real Space Mission Explorer

Hadil Ben Abdallah — Mon, 24 Aug 2026 09:09:25 +0000

I went through the documentation and feature list of React DataGrid, and I wrote React DataGrid: A Free, Open-Source React Data Grid with an Enterprise Edition (An AG Grid Alternative)

Now I wanted to use it the way I would use any other data grid in a real project: start with a real dataset, build useful interactions around it, push the grid with a large number of records, and see where things get difficult.

So I built a Space Mission & Satellite Explorer, a small flight-dynamics-style web application for exploring missions across different agencies, destinations, mission types, and decades.

The project works with a 100,000-mission live archive, while a 1,200-row client-side working set powers the interactive analysis experience. That gave me a good opportunity to test much more than basic sorting and pagination, including filtering, faceted search, grouping, pivoting, row pinning, custom cell renderers, virtual scrolling, and server-side infinite scrolling.

I also built two other parts of the application around the same data: a Mission Analytics page for aggregating and visualizing the dataset, and a Mission Details page where I used Tree Data to represent an individual mission's timeline.

This article is about what happened while building it, from setting up React DataGrid and configuring the first columns to working with 100,000 records and deciding whether I'd reach for it again in another data-heavy React project.

TL;DR

I built a Space Mission Explorer with React DataGrid to see how it would handle a real application.

The project uses a 1,200-row client-side dataset for interactive analysis and a 100,000-row live archive loaded through server-side infinite scrolling.

Along the way, I used features including:

multi-column sorting
quick search
faceted filtering
row grouping
row pinning
a Pivot Table builder
custom cell renderers
virtual scrolling
CSV/Excel export
Tree Data for Mission Timelines

The integration was smoother than I expected. The API felt familiar from the beginning; most of the features I needed worked as expected from the documented examples with relatively little adjustment, and switching between the smaller working dataset and the full archive stayed responsive.

There were still a few areas that required more digging, which I'll cover later, but overall, building the project gave me a better impression of React DataGrid than I could have gotten from reading its feature list.

What I Built: A Space Mission Explorer

I called the project an Orbital Index / Mission Data Terminal.

The idea was simple: take a large archive of fictionalized space-mission records and turn it into something developers could imagine using, a searchable, filterable interface where you can explore missions by agency, destination, status, mission type, launch date, duration, cost, and decade.

I deliberately chose this instead of building another generic CRUD dashboard because the dataset naturally creates the kinds of problems where a data grid becomes useful.

A mission record has enough structured fields to make filtering and sorting meaningful. Missions can be grouped by agency or destination. Costs and durations can be aggregated. And the mission itself has a natural hierarchy:

Launch → Earth Orbit → Translunar Injection → Lunar Orbit → Descent & Landing → Surface Operations → Return to Earth

That last part also gave me a reason to test Tree Data instead of adding it just to check another feature off a list.

The application ended up with three main pages:

Mission Explorer: The main data-grid interface for searching, filtering, grouping, pivoting, editing, and exploring the mission archive.
Mission Analytics: A chart-focused view that turns the same mission data into success rates, agency comparisons, destination distributions, duration statistics, and cost analysis.
Mission Details: An individual mission view with metadata, crew and equipment information, related dossiers, and a hierarchical mission timeline.

The Explorer is where most of my React DataGrid testing happened. The Analytics page uses the same 1,200-row working dataset and aggregation logic to produce visualizations, while the Details page gave me a completely different use case for the grid's hierarchical data capabilities.

For the stack, I used React, TypeScript, Tailwind CSS, React DataGrid, the mission dataset, and a charting library for the analytics view.

One quick transparency note before I dive into the grid: I vibe-coded a small part of the initial project setup to avoid spending a big chunk of my time building boilerplate that wasn't really the point of this experiment.

The goal here wasn't to prove that I could build an entire space-mission website from scratch; it was to spend my time actually using React DataGrid in a realistic project and see how it handled the data, interactions, and scale.

I deliberately kept the project small enough to understand from end to end but complex enough that a basic <table> would start becoming a problem.

You can check out the full project and explore the missions yourself.

Live Demo 👀

GitHub Repository ⭐

Setting Up React DataGrid

Once I had the project structure in place, I wanted to get the grid working before spending time on the rest of the interface. I started with the open-source package and kept the first render intentionally simple.

The installation was straightforward:

npm install react-open-source-grid

Then I imported the library's stylesheet:

import 'react-open-source-grid/dist/lib/index.css';

For the first test, I created a small grid with only a few mission fields:

const columns: Column[] = [
  { field: 'mission', headerName: 'Mission', width: 200 },
  { field: 'agency', headerName: 'Agency', width: 120 },
  { field: 'status', headerName: 'Status', width: 140 },
  { field: 'launchDate', headerName: 'Launch Date', width: 130 },
  { field: 'destination', headerName: 'Destination', width: 150 },
];

From there, I defined typed columns for Mission, Agency, Status, Launch Date, Destination, Mission Type, and Duration and mapped the dataset to them.

At this stage, the API felt familiar, so I didn't have to spend much time learning a completely unfamiliar grid model.

I also kept the React DataGrid GitHub repository and documentation close by while building, since this was a hands-on test.

Building the Mission Explorer

I didn't want to build a grid with a few rows just to say I had used one. I wanted enough data and enough interactions to see whether the component could handle the kind of complexity you'd expect in a real application.

I ended up using two dataset modes: a 1,200-row client-side working set for interactive analysis, virtual scrolling, and a 100,000-row live archive that loads records on demand through server-side infinite scrolling.

The difference between the two modes was useful because it gave me two different ways to work with the same mission data instead of artificially creating a huge dataset just for benchmarking.

Mission Explorer page

Sorting, Filtering, and Search

I started with the interactions I'd expect from any data grid. The column headers support sorting, including multi-column sorting, so I could do things like sort missions by agency and then by launch date without writing custom sorting logic myself.

For filtering, I had both the global search bar and individual column filters. The global search makes it easy to quickly find a mission, agency, or destination, while the filters directly under the column headers give me more control when I need to narrow down a specific field.

The sidebar filters were even more useful for this dataset. Instead of forcing me to type everything into a search box, the faceted search panel lets me filter by Agency, Status, Destination, Mission Type, and Decade. Each facet also displays live counts, such as NASA (265) or Successful (839), so I can immediately see how much data each filter represents.

If I want to see only successful NASA missions to Mars from the 1990s, I can narrow the dataset from several different dimensions without building a complicated filter UI around the grid myself.

I also tested column resizing and reordering, and both were straightforward to use when adjusting the Explorer to different screen sizes and workflows.

Sorting, Filtering, and Search

Grouping and Pinning

Once filtering was working, I wanted to see how the grid handled more analytical interactions.

React DataGrid lets me drag columns into the grouping area above the table. That means I can group missions by something like "Agency" and then further organize them by another field, instead of treating every mission as an isolated row.

Grouping and Pinning

I also pinned four reference missions to the top of the grid. This is a small feature, but I found it really useful when working with a large dataset because important records remain visible while I scroll through the rest of the archive.

Pivot Table

The most interesting part of the Explorer was the built-in Pivot Table builder.

Instead of manually writing aggregation logic for every analysis I wanted to perform, I could choose a Row Group By, Pivot Column, Value Column, and Aggregation, then apply the configuration directly from the interface. I could also toggle totals rows and a grand total column.

For example, I could group missions by agency, pivot them by destination, and aggregate values such as duration or cost. That turns the grid from a place where I simply browse records into something I can use to explore the dataset.

Pivot Table

Custom Cells, Totals, and Export

I also used custom cell rendering to make the grid easier to scan. Mission statuses aren't displayed as plain text; they're represented with color-coded badges for Successful, Planned, Partial Success, Failed, Cancelled, and in progress. The working set also includes a totals footer that aggregates values such as duration and cost.

Around the grid, I added the controls I would actually expect to use in a production data table: a column picker, CSV and Excel export, a layout reset control, and four density options ranging from Ultra Compact to Comfortable.

Custom Cells and Totals

CSV and Excel export

What I liked most was being able to combine faceted filtering, grouping, pivoting, pinned rows, custom renderers, and export in the same interface without the page turning into a collection of disconnected controls.

Building Mission Analytics

Once the Mission Explorer was working, I wanted to use the same dataset for something beyond browsing individual records. That's why I made the second page, Mission Analytics.

This page is deliberately chart-first. Instead of displaying another table, I used the same mission data and aggregation logic from the Explorer to create a set of visualizations that answer higher-level questions about mission history, agencies, destinations, and costs.

At the top, I added four summary cards:

1,200 missions in scope
79.6% average success rate
$669.12B in program cost
1,543 days average mission duration

Under those cards, the page contains five different visualizations: launch cadence by decade, agency reliability, destination distribution, success profile by mission type, and cost versus mission duration.

Mission Analytics page

Launch Cadence and Agency Reliability

The first chart looks at Launch Cadence by Decade, showing how the number of missions and successful missions changed across the different decades in the dataset.

This gives the archive a historical dimension that isn't obvious when you're looking at individual rows in the Explorer. Instead of asking which missions launched, I can start asking how mission activity changed over time.

Next, the Agency Reliability chart compares launch volume with average success rates across agencies such as NASA, SpaceX, Roscosmos, ESA, CNSA, ISRO, JAXA, and Blue Origin.

This is where the aggregation capabilities became useful. The chart isn't based on a separate dataset created just for the dashboard. It's derived from the same mission records I was already filtering, grouping, and analyzing in the Explorer.

Launch Cadence and Agency Reliability

Destination, Mission Type, and Cost

The other three visualizations look at different dimensions of the same data.

The Destination Distribution chart shows where missions in the archive are going, with destinations including Earth Orbit, the Moon, Mars, the Asteroid Belt, Deep Space, and Jupiter.

The Success Profile by Mission Type takes another angle by comparing success rates across mission types such as rovers, orbiters, flybys, robotic landers, space telescopes, and sample-return missions.

Finally, the Cost vs. Mission Duration scatter plot lets me look at whether expensive missions also tend to have longer durations. Each point represents an individual mission, making it easier to spot unusually expensive or long-running programs.

What I found interesting here is that I didn't need to put another grid on this page for React DataGrid to remain useful. The grid's underlying dataset and aggregation logic are still doing the work; I'm simply presenting the results in a form that's easier to interpret visually.

Destination, Mission Type, and Cost

Building the Mission Details Page

After working with the full archive, I wanted the third page to do the opposite: take me from 100,000 missions down to one specific mission.

For this example, I used MX-000006, Apollo VIII. The page brings the mission's main information together, including its launch date, duration, program cost, and crew, without forcing everything into another large table.

Mission Details Page

The most interesting part of this page, though, is the Mission Timeline.

Using Tree Data for a Real Mission Timeline

A mission isn't just a collection of unrelated fields. It naturally has an ordered structure: launch happens before orbital insertion, which happens before the mission's main operations, and so on.

That made the timeline a good place to use Tree Data.

For Apollo VIII, I structured the mission into 5 phases:

Apollo VIII
├── Launch
├── Orbit Insertion
├── Payload Commissioning
├── Operations
└── Deorbit

Each phase has its own COMPLETE status badge and a T+ day offset, so the timeline gives me both the hierarchy and the chronological context of the mission.

Tree Data for a Real Mission

This is one of the features that made more sense once I had a real application to build. I could have created a custom nested component for the timeline, but Tree Data already maps naturally to this kind of structured information.

The rest of the page contains the Crew Manifest, Payload & Equipment, and Related Dossiers sections. These keep the additional mission information accessible without turning the page into another dense data-management screen.

Crew Manifest, Payload & Equipment and Related Dossiers

At this point, I had all three parts of the application working together:

Mission Explorer for searching and manipulating the archive
Mission Analytics for understanding the data at a higher level
Mission Details for drilling into an individual mission

This gave me a better environment for evaluating React DataGrid than a small demo table would have. I had real filtering, grouping, pivoting, hierarchical data, custom rendering, and large datasets all working in the same project.

Theming & UI Customization

Once the functionality was working, I spent some time making the grid belong inside the application. The default data grid look would have worked, but it didn't really fit the dark, cyan-accented flight dynamics terminal style I was going for.

I used theme variables to adapt the grid's appearance and added custom cell renderers for the mission status badges. The badges use different colors for states such as Successful, Planned, Partial Success, Failed, Cancelled, and in progress, which makes scanning the Explorer much easier.

I also used four density modes, Ultra Compact, Compact, Normal, and Comfortable, so the amount of information displayed per row can be adjusted without rebuilding the grid layout.

What I appreciated here is that customization didn't require me to fight the component's default styling. Most of the work was about making React DataGrid match the visual language of the project rather than trying to work around the grid itself.

Developer Experience: What Was Easy and What Took More Work

The overall developer experience was one of the biggest positives from this project. Installation and getting the first working grid on screen took less time than I expected, and the API felt familiar.

Once the basic grid was running, adding sorting, filtering, grouping, and the Pivot Table builder was straightforward and aligned with the documented examples. The same was true for the Tree Data implementation on the Mission Details page. I wasn't constantly trying to figure out how to make the library do something it wasn't designed to do.

Accessibility was another area I paid attention to while working with the grid. I also tested keyboard navigation and paid attention to the grid's ARIA behavior while working through the interface. The documentation also gave me enough examples to understand the less common features I was using.

That said, not every part of the process was equally straightforward. The more specialized features required more time to understand than everyday operations such as sorting or filtering. The Pivot Table builder and Tree Data configuration were the areas where I spent more time checking examples and figuring out exactly how I wanted the data structured.

React DataGrid vs. a Basic HTML Table: What I Would Choose

If I were only displaying five or ten rows of static data, I wouldn't reach for React DataGrid. A basic HTML table or lightweight React table would be simpler and would do the job well.

This project was a very different situation.

Once I needed 100,000 missions, faceted search, multi-column sorting, grouping, pivoting, Tree Data, custom cell renderers, and server-side infinite scrolling, a basic table would have required me to build a large part of that functionality myself.

That's where React DataGrid made more sense. Instead of spending my time building and maintaining table infrastructure, I could focus on the actual application: how missions should be organized, what users should be able to explore, and how the analytics should work.

Is React DataGrid Worth Using?

After building a project with it, I think React DataGrid makes sense for applications where the data itself is a major part of the user experience.

I'd particularly consider it for:

Data-intensive dashboards
Analytics applications
Admin interfaces
Financial applications
Internal business tools
Applications with large datasets
Projects with complex filtering or grouping
Applications with hierarchical data
React applications that need server-side data loading

The Space Mission Explorer was a good test because it combined several of these requirements at once. I had a 1,200-row client-side working set for interactive analysis and a 100,000-mission live archive using server-side infinite scrolling.

Final Thoughts

Building the Space Mission Explorer gave me a better perspective on React DataGrid than I could have gotten from a feature checklist alone.

I was able to take a real dataset, turn it into an interactive explorer for a 100,000-row mission archive, build analytics around it, and use Tree Data for a mission timeline without having to build the grid infrastructure myself.

For data-heavy React applications, that's ultimately what matters:

The grid should handle the complexity of the data so you can focus on building the product around it.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

How to Reach Your Full Potential as a Programmer (It's Probably Not What You Think)

Hadil Ben Abdallah — Mon, 17 Aug 2026 13:05:25 +0000

Every programmer wants to improve.

We all dream about becoming the person who can look at a difficult problem, think for a few minutes, and confidently build a solution from scratch.

But whether you're writing your first "Hello, World!" program or you've been coding for years, you've probably asked yourself this question:

"How do I actually become a great programmer?"

The internet is full of advice.

One person tells you to build more projects. Another says you should solve hundreds of coding problems…

Then someone else insists that AI has changed everything and that learning has never been easier.

But the truth is…
There are no shortcuts.

The developers who keep improving year after year usually have something else in common: they build habits that make learning part of their daily routine.

Here are the habits that I believe make the biggest difference.

1. Go Beyond Tutorials and Learn the Fundamentals

Tutorials are a great place to start.

They help you build your first application, understand a new framework, or explore a technology you've never used before. We've all learned something valuable from a good tutorial.

But tutorials shouldn't become your entire learning strategy.

At some point, you need to understand why the code works instead of simply following someone else's steps. That's where books, official documentation, and software architecture resources become valuable.

The more you understand the reasoning behind a solution, the easier it becomes to solve completely new problems without searching for another tutorial.

2. Stop Treating Working Code as the Finish Line

We've all done it.

You find a solution online, paste it into your project, it works perfectly… and you immediately move on to the next task.

The problem is that your application improved, but your understanding didn't

Instead…

Get curious
Remove a line of code
Change a function
Replace one approach with another
See what breaks, then figure out why it broke

Those little experiments teach you far more than simply reading an article or watching a video.

3. Spend More Time Outside Your Comfort Zone

Growth feels exciting in hindsight.

While it's happening, it usually feels frustrating.

If you've been building the same type of application for months, using the same framework and the same programming language, you're probably becoming more efficient, but not necessarily becoming a better programmer.

Try something different.

Build an API if you've only worked on the frontend
Learn a systems language if you've only written Python
Explore mobile development, databases, networking, or cloud technologies

The goal isn't to master everything.
The goal is to keep giving your brain new problems to solve.

4. Take Ownership Instead of Waiting for Permission

The habit I've noticed in strong developers is that they don't always wait for interesting work to appear.

They look for opportunities to challenge themselves.

Maybe it's the feature everyone keeps postponing.
Maybe it's the bug nobody wants to investigate.
Maybe it's improving a tool the whole team relies on every day.

Those moments rarely come with a big announcement.

But they're often the projects that teach you the most because they force you to think beyond your comfort zone.

5. Don't Be Afraid of Open Source

Open source can look intimidating from the outside.

You see thousands of contributors, millions of lines of code, and assume you have nothing valuable to add.

In reality, it's much simpler than that.

Not every contribution needs to introduce a complex new feature.

Fixing documentation
Improving an example
Correcting a typo
Reporting a bug

They all matter.

Once you start contributing, you also begin learning how experienced developers organize projects, review code, and collaborate on software that people actually use.

That's an experience personal projects don't provide.

6. Surround Yourself with Developers Who Inspire You

Programming isn't something you have to learn alone.

Some of the biggest improvements in my own journey came from reading other developers' code, joining technical communities, and paying attention to how experienced engineers approached problems.

Being around people with higher standards naturally pushes you to improve your own work.

It might be an open-source community, a local meetup, Discord, a DEV community, or even a colleague whose code you admire.

Being part of those communities exposes you to ideas, feedback, and different ways of thinking that you probably wouldn't discover on your own.

Sometimes a single conversation can completely change the way you think about software development.

7. Teach What You Learn

One of the fastest ways to discover what you actually understand is trying to explain it to someone else.

The moment you write a blog post, answer a question online, or help a teammate solve a problem, you quickly notice the parts you're still unsure about.

That's not a weakness. It's part of the learning process.

Teaching forces you to organize your thoughts, simplify complex ideas, and fill the gaps in your own understanding.

And along the way, you might help another developer who's just starting the same journey.

So, How Do You Actually Become a Better Programmer?

There isn't one secret technique.

No single programming language, framework, course, or AI tool will suddenly make you an exceptional developer.

Real progress comes from combining small habits and repeating them consistently.

Read beyond tutorials
Experiment with your code
Challenge yourself with unfamiliar problems
Take ownership
Contribute to open source
Learn from developers who push you to improve
Share what you learn with others

Final Thoughts

Every programmer's journey is different.

Some people learn quickly because they have great mentors. Others figure things out through countless experiments, mistakes, and late-night debugging sessions.

No matter where you are today, reaching your potential isn't about knowing every programming language or memorizing every framework.

It's about staying curious, asking better questions, and never becoming too comfortable with what you already know.

The best programmers aren't the ones who never get stuck.
They're the ones who never stop learning.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

React DataGrid: A Free, Open-Source React Data Grid with an Enterprise Edition (An AG Grid Alternative)

Hadil Ben Abdallah — Tue, 11 Aug 2026 09:09:04 +0000

If you've ever needed to build a serious data table in React, you've probably run into this problem. React doesn't include a native data grid component, so you're usually left choosing between paying for an enterprise solution or spending weeks building advanced table functionality yourself.

For small datasets, a simple HTML table works well enough. But once you need virtualization, row grouping, inline editing, advanced filtering, server-side data loading, or richer keyboard interactions, things become much more complicated. Those features require significant engineering effort if you decide to build everything from scratch.

That's exactly where React DataGrid, an open-source React data grid with both free and Enterprise offerings, comes in.

In this article, I'll walk through what makes a production-ready React data grid, introduce React DataGrid, show how to get started with its open-source edition, explore its standout features, explain what's available in the Enterprise edition, and compare it with AG Grid so you can decide which option is right for your project.

TL;DR

If you're looking for a React data grid that combines an open-source MIT-licensed core with an optional Enterprise edition, React DataGrid is worth considering.

It's an open-source React data grid built with React 18, TypeScript, and Tailwind CSS, designed for everything from simple data tables to large, data-intensive applications. The free edition provides a broad set of data-grid capabilities, while the Enterprise edition adds commercial features for teams that need more advanced workflows, data operations, and enterprise functionality.

Here's what React DataGrid offers:

✅ MIT-licensed, open-source core that is free to use.
✅ Virtual scrolling for datasets with 100,000+ rows and 200+ columns.
✅ Server-side infinite scrolling for datasets with 100M+ rows, including server-side filtering, sorting, pagination, intelligent block caching, request concurrency, and LRU cache eviction.
✅ Row grouping and aggregation for organizing and analyzing complex datasets.
✅ Tree Data for hierarchical rows and expandable parent-child structures.
✅ Market data support for applications that receive frequent live updates.
✅ 10 built-in themes, custom cell renderers, layout persistence, density modes, and extensive customization APIs.
✅ Accessibility support with WCAG 2.1 AA compliance and a published VPAT 2.4 report.
✅ Enterprise features including Server-Side Row Model, Master/Detail, Formula Engine, Undo/Redo, Range Selection, Clipboard (TSV), Fill Handle, Transactions, Cell Permissions, Audit Trail, Row Locking, Excel/CSV Import, PDF Export, Filter Presets, Saved Views, and Form Editor.
✅ A familiar AG Grid-style API that can make adoption easier for developers already working with enterprise data grid libraries.

What Are React Data Grid Components?

A React data grid component is a specialized table component designed to display and manage large, interactive datasets. Unlike a basic HTML table, it typically includes features such as sorting, filtering, pagination, row selection, inline editing, keyboard navigation, and virtualization.

A traditional HTML <table> is perfectly fine for displaying a few dozen rows of static data. The problem appears once datasets start growing or users need richer interactions.

Rendering thousands of rows can slow down the browser, and implementing advanced behaviors like filtering, editing, or drag-and-drop often means writing and maintaining a significant amount of custom code.

That's why most production React applications rely on dedicated data grid libraries. They solve performance challenges, provide a consistent user experience, and include many of the features that enterprise dashboards, admin panels, analytics platforms, and internal business applications depend on every day.

What Features Really Matter in a React Data Grid?

The difference between a basic data table and a production-ready React data grid comes down to five areas: performance, data operations, server-side capabilities, accessibility, and customization. These are also the features that many free libraries either limit or don't offer at all.

When evaluating a React data grid, here are the capabilities that deserve the most attention:

Performance at scale through virtual scrolling so tens of thousands of rows remain smooth to navigate.
Advanced data operations like row grouping, aggregation, filtering, multi-column sorting, and inline editing.
Server-side support for massive datasets using infinite scrolling, server-side filtering, sorting, and intelligent caching.
Accessibility with keyboard navigation, screen-reader compatibility, and WCAG compliance.
Customization through themes, custom cell renderers, layout persistence, and flexible APIs that fit your application's design system.

Many open-source React grids do one or two of these things well.

Performance might be excellent, but advanced grouping is missing. Another library may support editing but fall short when datasets become truly large.

React DataGrid was built to check every one of those boxes.

Introducing React DataGrid

React DataGrid is an open-source React data grid built with React 18, TypeScript, Tailwind CSS, and Vite. Its open-source core is MIT licensed and published as the react-open-source-grid npm package, while the project also offers an Enterprise edition with additional commercial features. The API is intentionally familiar to developers who have previously worked with AG Grid, which can make the learning curve smaller.

React DataGrid is part of the broader GridEngine platform, which provides data grid solutions for different technologies and use cases.

One thing I particularly like is that the project doesn't expect you to install it blindly. Before writing any code, you can explore a public live demo, browse the documentation, and inspect the GitHub repository to see exactly what the library offers.

That transparency is valuable, especially when you're evaluating a component that may become the backbone of your application's data layer.

Installation & Quick Start

Getting started with React DataGrid is straightforward. If you're starting a new React project or adding a data grid to an existing application, you can install the package and have a working grid running in just a few minutes.

The setup is intentionally minimal so you can focus on your data instead of configuration.

Install the package:

npm install react-open-source-grid

Then import the stylesheet:

import 'react-open-source-grid/dist/lib/index.css';

Finally, create your first grid:

import { DataGrid } from 'react-open-source-grid';
import type { Column, Row } from 'react-open-source-grid';

const columns: Column[] = [
  { field: 'id', headerName: 'ID', width: 70 },
  { field: 'name', headerName: 'Name', width: 180, editable: true },
];

const rows: Row[] = [
  { id: 1, name: 'John Doe' },
  { id: 2, name: 'Jane Smith' },
];

export default function App() {
  return (
    <DataGrid
      columns={columns}
      rows={rows}
    />
  );
}

From this simple starting point, you can gradually enable more advanced capabilities like grouping, virtual scrolling, custom renderers, server-side infinite scrolling, and layout persistence, while keeping the same underlying grid as your application grows.

Core Features of React DataGrid

React DataGrid stands out among open-source React data grid libraries because it doesn't just offer a long feature checklist. The open-source core gives you a broad foundation for everyday data-intensive applications, while the Enterprise edition provides additional capabilities for more advanced workflows as your requirements evolve.

Let's look at the capabilities that make the biggest difference in real-world React applications.

Everyday Grid Features You'll Actually Use

Every data grid should make everyday interactions feel effortless, and React DataGrid's open-source core covers a broad set of essential capabilities without requiring additional plugins or a commercial license.

Out of the box, you get:

Click-to-sort columns
Per-column filtering with advanced filtering options
Pagination with 10, 20, or 50 rows per page
Drag-to-resize columns
Drag-and-drop column reordering
Sticky column headers
Full keyboard navigation
Single and multiple row selection
Double-click inline editing
Built-in quick filter for searching across the entire grid

Beyond those essentials, React DataGrid also includes several quality-of-life features that become valuable in larger applications. APIs such as rowStyle, rowClass, and getRowHeight make it easy to highlight overdue invoices, completed tasks, priority tickets, or any other rows that deserve extra visual attention.

React DataGrid live demo with filtering, sorting, and status badges

Row Grouping and Aggregation for Complex Data

Row grouping is one of those features that developers often don't realize they need until users ask for it. When datasets become more complex, grouping records by department, status, region, customer, or category makes analysis much easier than scrolling through hundreds or thousands of individual rows.

React DataGrid lets users simply drag a column into the grouping area to organize data into expandable groups. Beyond grouping itself, the library also supports aggregation footers at two different levels.

You can display subtotals for each individual group as well as a grand-total footer for the entire dataset, with built-in aggregation functions including:

Sum
Average
Minimum
Maximum
Count

Both group-level and grid-level aggregation are included in the open-source grid. That's noteworthy because similar functionality is often associated with commercial data grid offerings.

Besides row grouping, React DataGrid also supports tree data, allowing you to display hierarchical datasets with expandable parent and child rows. This is useful for cases such as organizational charts, file explorers, category trees, or nested project structures.

React DataGrid Tree Data Demo for folder and file structure with nested directories

Built for Scale: Virtual Scrolling, Market Data, and Server-Side Infinite Data

Performance is often the first thing users notice when working with large datasets. A data grid can have every feature imaginable, but if scrolling feels sluggish or interactions lag, the overall experience quickly suffers.

React DataGrid tackles this challenge with two complementary approaches, depending on the size of your data.

For large datasets that can still live in the browser, the library supports virtual scrolling. Instead of rendering every row and column into the DOM, it only renders what's currently visible on screen.

This enables smooth performance with 100,000+ rows and more than 200 columns, while reducing rendering overhead and memory usage compared to rendering everything at once. The documentation also reports up to 100× faster rendering, 100× lower memory usage, and smooth 60 FPS scrolling in virtual mode.

React DataGrid Virtual Scrolling Demo with 100,000 rows & 200 columns

Performance isn't only about handling large datasets; it also matters when the data is constantly changing. React DataGrid includes support for real-time market data cases, making it suitable for applications that receive frequent live updates while remaining responsive.

If you're building financial dashboards, trading platforms, monitoring systems, or live analytics applications, the grid is designed to process continuous data updates while preserving smooth scrolling and a responsive user experience. This makes it a good option for applications where users need to monitor rapidly changing information without sacrificing performance.

React DataGrid live streaming updates Demo

For applications dealing with truly massive datasets, React DataGrid goes a step further with its free Server-Side Infinite Scroll capability. The InfiniteScrollDataGrid component works with a ServerSideDataSource to request only the data users actually need while scrolling, instead of loading millions of records into the browser at once.

This free server-side mode includes:

Server-side filtering, sorting, and pagination
Intelligent block caching
Configurable request concurrency
LRU cache eviction
Efficient handling of datasets with 100 million or more rows

Despite all of those capabilities, the API is simple:

const dataSource = new ServerSideDataSource({
  blockSize: 100,

  getRows: async (request) => {
    const response = await fetch("/api/data", {
      method: "POST",
      body: JSON.stringify(request),
    });

    return response.json();
  },
});

Instead of forcing developers to build custom pagination or infinite loading logic, the component handles the heavy lifting while your backend simply returns the requested records.

For applications that need more advanced server-side data-management capabilities, React DataGrid also offers a separate Server-Side Row Model as part of its Enterprise features.

Enterprise Features for Advanced Workflows

React DataGrid also offers an Enterprise edition for teams that need capabilities beyond the open-source core.

The Enterprise tier adds a collection of advanced features designed for more sophisticated data workflows, including server-side data management, transactional editing, controlled access, auditability, data import and export, and advanced record management.

Some of the Enterprise features include:

Server-Side Row Model for applications that need more advanced server-side data management and block-based data fetching for large datasets.
Master/Detail for expanding records and displaying related or nested information.
Formula Engine for Excel-style formulas directly inside grid cells.
Undo/Redo for multi-step reversible edits.
Range Selection for selecting Excel-style cell ranges.
Clipboard (TSV) for copying and pasting tabular data with spreadsheet-style workflows.
Fill Handle for dragging to fill or copy cell values across a range.
Transactions for staged add, update, and remove operations.
Cell Permissions for controlling per-cell editing and data visibility.
Audit Trail for maintaining an immutable record of edits.
Row Locking for collaborative workflows where specific rows need to be locked.
Excel/CSV Import for mapping, validating, and coercing imported data.
PDF Export for generating branded, paginated documents from grid data.
Filter Presets with an AND/OR filter builder and reusable filtering configurations.
Saved Views for storing personal or shared grid layouts.
Form Editor for editing records through a dedicated slide-in form interface.

These features are particularly useful when a data grid starts acting as an interactive workspace for business operations.

For example, a financial application might use the Formula Engine and Range Selection for spreadsheet-like workflows, while a business application with stricter data controls could combine Transactions, Cell Permissions, Audit Trail, and Row Locking to provide more controlled data management.

The Enterprise edition therefore gives teams a path to move from a capable open-source data grid to a more advanced commercial data-management experience without having to replace the underlying grid technology.

The open-source core also includes several features that make day-to-day development easier, such as computed value getters, custom formatters, flexible column sizing, conditional cell styling, shared default column definitions, built-in tooltips, loading overlays, and "no rows" states.

Taken together, the open-source core and Enterprise edition give React DataGrid a broader range of capabilities while allowing teams to choose the level of functionality that matches their requirements.

Accessibility and Enterprise Compliance

Accessibility is an area where many open-source UI libraries still fall short. Keyboard navigation may be partially implemented, screen readers may receive limited support, and formal accessibility documentation is often missing altogether.

React DataGrid stands out by treating accessibility as a core feature, not just an afterthought.

The grid is WCAG 2.1 AA compliant and includes full keyboard navigation, ARIA support, and screen-reader compatibility. It also publishes a VPAT 2.4 accessibility report along with Section 508 documentation, making it much easier for organizations with accessibility requirements to evaluate React DataGrid before adopting it.

While individual developers certainly benefit from accessible components, this kind of documentation becomes more valuable in larger companies where procurement, legal, or compliance teams often review third-party dependencies before they're approved for production use.

Theming, Customization, and Developer Experience

A data grid rarely exists in isolation. It needs to blend naturally into the rest of your application's design system, support custom business logic, and remain flexible as requirements evolve.

React DataGrid provides 10 built-in themes, covering both light and dark interfaces, including Quartz, Alpine, Material, Nord, Dracula, Solarized (Light and Dark), Monokai, One Dark, and a standard Dark theme.

Theme switching is powered by CSS variables, making it easy to match the rest of your application without extensive styling work. Themes can be switched instantly while preserving consistent spacing, typography, and component styling.

React DataGrid theme system

Customization goes well beyond colors. The library also provides:

A custom cell renderer framework for badges, progress bars, images, buttons, charts, and icons.
A right-click context menu with actions like copy, export, pin/unpin, auto-size, hide columns, and filter by value.
Layout persistence using localStorage, server storage, or user profile storage.
Multiple density modes ranging from Ultra Compact to Comfortable.
Value getters and formatters for computed columns.
Conditional styling through cellStyle, cellClass, and cellClassRules.
Shared column configuration with defaultColDef and built-in default sorting.
Tooltips, loading overlays, and no-data overlays.
Declarative column visibility for showing or hiding columns based on user roles or application state.

Beyond the getting-started guides, the project includes a documented Grid API with more than 100 methods, along with interactive examples demonstrating how those APIs work. Enterprise capabilities build on this foundation with additional functionality for advanced data workflows.

How React DataGrid Compares to AG Grid

AG Grid Community is a solid open-source grid, but many of its most powerful capabilities, including row grouping, server-side data handling, pivoting, and several advanced data operations, are reserved for the commercial Enterprise edition.

React DataGrid takes a different approach by making many of those enterprise-style features available under the MIT license.

Here's a side-by-side comparison:

Feature	AG Grid Community	AG Grid Enterprise ($$)	React DataGrid Free	React DataGrid Enterprise ($$)
License	MIT	Commercial	MIT	Commercial
Virtual Scrolling	✅	✅	✅	✅
Tree Data	❌	✅	✅	✅
Row Grouping	❌	✅	✅	✅
Server-Side Infinite Scroll	❌	✅	✅ (100M+ rows)	✅
Server-Side Row Model	❌	✅	❌	✅
Aggregation Footers	❌	✅	✅	✅
Context Menus	❌	✅	✅	✅
Advanced Filtering	Basic	✅	✅	✅
Faceted Search / Token Search	❌	✅	✅	✅
Row Pinning	❌	✅	✅	✅
Master/Detail Rows	❌	✅	❌	✅
Pivot Table	❌	✅	✅	✅
Integrated Charts	❌	✅	✅	✅
Advanced Cell Editors	❌	✅	✅	✅
Excel Export	❌	✅	❌	✅
Formula Engine	❌	❌	❌	✅
Undo / Redo	❌	❌	❌	✅
Range Selection	❌	❌	❌	✅
Clipboard (TSV)	❌	❌	❌	✅
Fill Handle	❌	❌	❌	✅
Transactions	❌	❌	❌	✅
Cell Permissions	❌	❌	❌	✅
Audit Trail	❌	❌	❌	✅
Row Locking	❌	❌	❌	✅
Excel / CSV Import	❌	❌	❌	✅
PDF Export	❌	❌	❌	✅
Filter Presets	❌	❌	❌	✅
Saved Views	❌	❌	❌	✅
Form Editor	❌	❌	❌	✅
Accessibility (WCAG 2.1 AA + VPAT)	Partial	✅	✅	✅
Built-in Themes	1	Multiple	10	10

This doesn't necessarily mean React DataGrid should replace AG Grid in every project. AG Grid remains one of the most mature data grid solutions available and comes with commercial support for organizations that require enterprise SLAs.

However, if your team needs a capable open-source React data grid with features such as grouping, aggregation, virtualization, theming, and rich customization, React DataGrid's MIT-licensed core presents a compelling option.

When requirements expand to advanced workflows, auditing, permissions, transactions, or spreadsheet-style operations, the Enterprise edition provides an upgrade path without requiring you to switch to a different grid component.

Who Should Use React DataGrid?

React DataGrid isn't trying to be the right data grid for every React project, and that's actually one of its strengths.

I think it's well suited for:

Internal business dashboards
Admin panels
SaaS applications
CRM and ERP systems
Financial dashboards
Analytics platforms
Inventory management systems
Applications that need grouping, aggregation, virtualization, rich customization, or Server-Side Infinite Scroll for large datasets without paying for a commercial grid
Enterprise applications that may need advanced capabilities

If your application starts with a few hundred rows today but could eventually grow into tens of thousands, or even millions, of records, it's nice to know you won't need to replace your grid component later just because your data has outgrown it.

📌 If you find this useful, consider starring the GitHub repo; it helps support the open-source project and its continued development.

⭐ Star React DataGrid on GitHub

Final Thoughts

What stands out about React DataGrid isn't a single headline feature; it's the combination of an open-source core and an optional Enterprise edition that can grow with an application's requirements.

The MIT-licensed core provides developers with a capable React data grid for everyday data management, including sorting, filtering, grouping, aggregation, virtualization, theming, customization, and accessibility.

For teams that need more advanced workflows, the Enterprise edition adds capabilities such as Server-Side Row Model, Master/Detail, Formula Engine, Transactions, Cell Permissions, Audit Trail, Row Locking, Excel/CSV Import, PDF Export, Filter Presets, Saved Views, and Form Editor.

That gives teams a straightforward choice:

Start with the free, open-source grid for projects that don't need commercial features
Move to the Enterprise edition when more advanced data operations and workflows become necessary.

This article focused on what React DataGrid offers and how its free and Enterprise capabilities compare. In my next article, I'll take a different approach by building a real React project with it, sharing the complete development experience, highlighting what worked well, and discussing any challenges I encounter along the way. Stay tuned!

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

AI Agents for Marketing in 2026: 8 Platforms Replacing Manual Marketing Ops

Hadil Ben Abdallah — Wed, 05 Aug 2026 09:02:32 +0000

According to HubSpot's 2026 State of Marketing Report, 61% of marketers believe AI is creating the biggest disruption marketing has experienced in the last 20 years, while 80% of marketers are already using AI for content creation and 75% are using it for media production. The shift is all about how effectively marketing teams can turn AI from a productivity assistant into an autonomous execution layer.

That shift explains why AI agents for marketing (also called autonomous marketing platforms) have become one of the fastest-growing categories in marketing technology.

Unlike AI assistants that simply generate blog posts, suggest campaign ideas, or summarize performance reports, AI marketing agents are designed to monitor real-time signals, make decisions, execute workflows, optimize campaigns, and continuously adapt based on outcomes.

For growth teams trying to increase output without constantly expanding their operations team, this represents a fundamental change.

In this guide, we'll look at the eight best AI agents for marketing in 2026, compare where each platform fits, examine their limitations, and explore which solutions can replace parts of the manual marketing operations workload.

Quick Comparison: Best AI Agents for Marketing in 2026

Before diving into each platform, here's a side-by-side comparison of the leading AI marketing agents and autonomous marketing platforms available today.

Platform	Category	Best For	Pricing	Key Limitation
Jasper AI	AI content generation	Scaling blogs, ads, email campaigns, and brand-consistent content	Paid	Generates content but doesn't execute campaigns or react to marketing signals
Hellyeah	AI-powered marketing platform	Teams wanting autonomous paid media operations and AI-powered marketing workflows	Enterprise	Requires onboarding, event instrumentation, and setup before delivering value
Smartly.io	Paid social automation	Large paid social teams managing Meta and TikTok campaigns	Paid / Enterprise	Focused on paid social only
HubSpot AI	CRM + AI marketing suite	SMBs wanting CRM, email marketing, and AI assistance in one platform	Free / Paid / Enterprise	AI assists users instead of operating autonomously
Persado	AI messaging optimization	Enterprise email and advertising teams	Enterprise	Focuses only on copy optimization
Clay	AI outbound personalization	B2B sales and outbound marketing	Paid	Outbound-focused rather than full marketing operations
Anyword	AI writing + predictive scoring	Teams optimizing marketing copy before publishing	Free / Paid	Requires humans to approve and publish content
n8n	Workflow automation + AI agents	Engineering-led organizations building custom AI workflows	Free / Paid	Requires technical resources to build and maintain

Although every platform on this list uses AI, they don't solve the same problem.

Understanding the differences between them is important because the right choice depends far more on what part of your marketing operation you want to automate than on which platform has the most AI features.

What "Replacing Manual Marketing Ops" Means

When people hear that AI agents are replacing marketing operations, it's easy to assume the goal is to replace marketers themselves. In reality, that's not what these platforms are designed to do.

Marketing teams still need people to:

define positioning
understand customers
shape brand strategy
approve messaging
make business decisions

Those responsibilities depend on creativity, judgment, and market knowledge that AI cannot replace.

What AI increasingly takes over is everything that happens after those strategic decisions have already been made. Once a campaign has been planned, someone still needs to launch it, build audience segments, monitor bids, rotate creatives, prepare reports, analyze experiments, watch for churn signals, schedule lifecycle campaigns, and research prospects before outbound outreach begins.

Individually, none of those activities are especially difficult. Together, however, they consume a significant portion of a marketing team's week, leaving less time for strategy and growth initiatives. AI marketing agents are designed to automate that execution layer so marketers spend less time operating systems and more time making decisions that actually move the business forward.

Instead of requiring someone to manually monitor dashboards throughout the day, these platforms continuously observe performance signals, decide what action makes sense based on current conditions, execute that action automatically, and evaluate the results before making future decisions.

That creates three broad categories of AI marketing platforms.

Category	What It Does	Examples
Single-function AI agents	Automate one marketing operation exceptionally well	Jasper AI, Clay, Persado, Anyword
Multi-function marketing platforms	Combine several connected marketing capabilities into one platform	HubSpot AI, Smartly.io
Full-stack agentic marketing platforms	Coordinate AI-powered marketing workflows and paid media operations from a unified platform	Hellyeah

The distinction matters because many products now advertise themselves as AI-powered, even though they still function primarily as assistants that help marketers work faster.

True AI marketing agents go much further by operating independently once objectives, constraints, and data are in place, allowing teams to automate entire operational workflows.

1. Jasper AI

One of the biggest challenges marketing teams faced when generative AI became mainstream wasn't simply creating content; it was producing large volumes of content without losing consistency across different writers, campaigns, and channels.

That's the problem Jasper AI is built to solve.

Jasper allows organizations to train a Brand Voice using their existing content. Once that foundation is established, the platform can generate blog posts, landing pages, email campaigns, advertising copy, product descriptions, and social media content that closely matches the company's writing style.

Its Campaigns feature also helps teams create multiple marketing assets from a single campaign brief, making it much easier to produce coordinated content for different channels without starting from scratch each time. For organizations publishing at scale, that significantly reduces production time while maintaining a consistent brand voice across the entire marketing team.

Best for

Marketing teams, content marketers, and demand generation teams producing large volumes of written marketing content across multiple channels.

Limitation

Jasper excels at generating content, but it doesn't operate your marketing stack. It won't manage advertising campaigns, monitor customer behavior, optimize budgets, trigger lifecycle campaigns, or react to performance signals automatically.

Pricing

Paid.

2. Hellyeah

Many companies don't struggle because they're missing marketing tools; they struggle because every marketing function lives inside a different one. Paid advertising, content operations, analytics, and reporting often run across separate platforms, leaving someone on the team responsible for connecting everything, interpreting the data, and managing the day-to-day execution that keeps campaigns running.

Unlike traditional marketing platforms that gradually added AI features over time, Hellyeah AI is an AI-native growth engine designed to reduce marketing operations overhead. Rather than simply helping marketers complete individual tasks faster, it automates repetitive marketing execution so teams can spend less time managing campaigns and more time focusing on strategy, creativity, and growth.

Today, Hellyeah centers on two core products. AIMA continuously optimizes paid media by monitoring performance, adjusting bids, reallocating budgets, and responding to conversion signals, while CLI provides an AI-powered interface for interacting with marketing workflows and operational data more efficiently. Together, they help reduce repetitive marketing work while keeping marketers in control of business decisions.

AIMA: Replacing Daily Paid Media Operations

Managing paid advertising requires constant optimization. Budgets need to be reallocated, bids adjusted, creatives refreshed, and audience performance monitored as campaign data changes throughout the day. For growing marketing teams, keeping up with those decisions manually can quickly become a full-time job.

AIMA automates that optimization layer by continuously monitoring campaign performance, interpreting conversion signals, adjusting bidding strategies, reallocating budgets, and helping teams respond to changing campaign performance. Marketing teams remain responsible for campaign strategy and business goals, while AIMA handles much of the repetitive execution required to keep campaigns performing efficiently.

CLI: AI-Powered Marketing Operations

Hellyeah's CLI provides an AI-first interface for interacting with marketing workflows, making it easier to execute operational tasks without navigating multiple dashboards or manually coordinating different systems. Instead of relying solely on traditional interfaces, teams can use AI to access marketing capabilities more efficiently and streamline day-to-day operations.

Combined with AIMA, the CLI helps reduce the operational overhead of running modern marketing by making routine execution faster and more accessible, while marketers continue focusing on strategy, messaging, and business decisions.

Best For

Growth-stage SaaS companies, enterprise B2B organizations, e-commerce businesses, fintech companies, gaming companies, mobile apps, and education platforms that want to reduce marketing operations overhead through AI-powered paid media optimization and modern AI-driven marketing workflows.

Limitation

Hellyeah is not a plug-and-play platform that delivers full autonomous execution on the first day.

Like any serious agentic marketing system, it requires clean event instrumentation, clearly defined business objectives, and an onboarding period so the platform can understand your business objectives, campaign structure, and operational workflows. Teams willing to invest in that foundation gain a platform that becomes more effective as more operational data becomes available over time, but organizations expecting immediate results without preparation may find the initial setup more involved than simpler marketing tools.

Pricing

Enterprise.

3. Smartly.io

Managing paid social campaigns at an enterprise scale involves much more than launching advertisements. Marketing teams constantly monitor campaign performance, adjust bids, test audiences, rotate creative assets, optimize budgets, and compare results across multiple advertising platforms. As campaign volume grows, those operational responsibilities quickly become too time-consuming for manual management alone.

Smartly.io helps automate much of that workload by combining creative production, campaign management, audience testing, bid optimization, and performance analysis inside a single platform. Its AI capabilities identify high-performing creative combinations, optimize campaign delivery across channels such as Meta and TikTok, and automatically allocate additional budget toward stronger-performing campaigns as new performance data becomes available.

The platform generates copy for landing pages, paid advertisements, emails, product descriptions, social media posts, and other marketing assets while assigning predictive performance scores to every variation. Those predictions are based on historical marketing data, giving teams another layer of confidence when comparing headlines, calls to action, subject lines, and advertising copy before launching a campaign.

While those scores don't replace real-world testing, they can help marketers prioritize stronger candidates earlier in the content creation process and reduce the number of weak variations that ever reach production.

Best for

Marketing teams producing high volumes of advertising, website, email, and social media copy that want performance insights before publishing.

Limitation

Anyword remains an AI-assisted writing platform rather than an autonomous marketing system. Human marketers still decide which content to approve, edit, publish, and distribute, and the platform doesn't execute campaigns or optimize marketing operations after the content has been created.

Pricing

Free / Paid.

8. n8n + AI Nodes

Not every company wants an opinionated marketing platform with predefined workflows. Many engineering-led organizations prefer building their own automation infrastructure so it integrates perfectly with existing internal systems, data sources, and business logic.

That's where n8n stands out.

As an open-source workflow automation platform, n8n connects hundreds of services through visual workflows while allowing developers to incorporate LLM-powered AI nodes into decision-making processes. Teams can build custom AI-powered workflows for campaign approvals, lead routing, CRM synchronization, reporting, content production pipelines, customer notifications, and countless other marketing operations.

Because the platform is self-hostable and highly customizable, organizations retain full ownership over their automation stack instead of relying entirely on a managed SaaS platform. That flexibility makes n8n especially attractive for companies with strong engineering teams and complex internal requirements.

Best for

Engineering-led organizations that want to design, build, and maintain highly customized AI-powered marketing workflows using their own infrastructure.

Limitation

The flexibility that makes n8n so powerful also makes it more demanding to implement. Building, maintaining, debugging, and continuously improving AI workflows requires dedicated engineering resources, making it less suitable for marketing teams looking for a ready-to-deploy autonomous marketing platform.

Pricing

Free / Paid.

How to Evaluate an AI Marketing Agent Platform

Not every platform marketed as an AI marketing solution is really an AI agent.

Many products still function as AI assistants that generate recommendations while leaving marketers responsible for execution. True AI agents go much further by observing real-time signals, making decisions, carrying out those decisions, and continuously improving based on the outcomes they observe.

If you're evaluating an AI agent for marketing, these are the questions that matter most.

Evaluation Criterion	What to Ask	Red Flag
Autonomy	Does the platform make decisions autonomously or simply recommend actions?	It only suggests optimizations that humans must review and approve.
Coverage	Does it automate one marketing function or multiple connected operations?	It automates a single workflow while claiming to replace an entire marketing team.
Real-Time Signals	Does it continuously monitor product, CRM, behavioral, and advertising data?	It relies on scheduled imports, CSV uploads, or delayed reporting.
Learning Loop	Does the system improve future decisions based on previous outcomes?	Performance improves only when humans manually update rules or workflows.
Setup Requirements	What onboarding, instrumentation, and data preparation are required before deployment?	It promises complete autonomous marketing within minutes and without preparation.

When campaign performance, customer behavior, experimentation results, and workflow execution all inform one another, improvements don't remain isolated inside individual tools. Instead, every successful outcome strengthens future decisions across the entire marketing operation, creating a compounding effect that's difficult to achieve with disconnected software.

Frequently Asked Questions

What are AI agents for marketing?

→ AI agents for marketing (also called autonomous marketing platforms) are AI systems that continuously monitor marketing signals, make decisions, execute campaigns, and learn from the outcomes without requiring marketers to initiate every action manually. Unlike traditional automation platforms that follow predefined rules, AI agents adapt their behavior as new data becomes available.

Can AI agents replace a marketing team?

→ Not entirely.

AI agents are designed to replace much of the marketing execution layer, including campaign optimization, reporting, audience management, experimentation, lifecycle automation, and many repetitive operational tasks. Human marketers remain responsible for strategy, positioning, creative direction, customer research, brand management, and the business decisions that require judgment, context, and creativity.

What's the difference between marketing automation and AI marketing agents?

→ Traditional marketing automation executes workflows that humans configure in advance using predefined rules.

AI marketing agents continuously evaluate current conditions, decide which action is most appropriate, execute that action automatically, and refine future decisions based on the results they observe. In other words, automation follows instructions, while AI agents determine what the next instruction should be.

Which AI marketing platform is best in 2026?

→ The answer depends on the part of marketing you're trying to automate.

If your priority is AI-assisted content generation, Jasper AI and Anyword are good choices. Clay is a strong option for outbound personalization, while Smartly.io remains one of the leading platforms for paid social automation.

If you're looking for a purpose-built platform that brings paid acquisition, lifecycle marketing, continuous experimentation, and custom workflows together into one connected autonomous system, Hellyeah is the strongest fit because it was designed specifically to automate the broader marketing operations layer rather than a single function.

Final Thoughts

Marketing teams aren't adopting AI agents simply because they want to work faster.

They're adopting them because modern marketing operations have become too complex for people to monitor every campaign, workflow, customer signal, and experiment manually throughout the day.

The biggest shift isn't that AI writes content or recommends optimizations. It's that an increasing share of the operational work, monitoring campaigns, adjusting budgets, reacting to customer behavior, coordinating workflows, and continuously testing new ideas, can now happen autonomously while marketers focus on higher-value decisions.

The right platform ultimately depends on what you're trying to automate. Some teams need better content creation, others need smarter outbound prospecting or paid media optimization, while larger organizations may benefit from platforms that automate broader marketing operations.

Understanding those differences will help you choose a solution that fits your workflow instead of adding another disconnected AI tool to your stack.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Hellyeah is an autonomous AI growth platform that runs and optimizes marketing operations in real time. It helps companies scale faster by turning their entire growth engine into a continuously learning, always-on system.

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

5 Most Important Programming Languages to Learn in 2026 (Based on Real Industry Demand)

Hadil Ben Abdallah — Mon, 03 Aug 2026 09:05:57 +0000

Every year, developers ask the same question:

"Which programming language should I learn next?"

And every year, the internet gives us a hundred different answers.

One person says AI will replace programmers, so learning any language is pointless. Meanwhile, others are arguing that you don't even need to learn programming anymore because AI already writes half the code. (I actually shared my thoughts on that in my article about whether you should still learn coding in the age of AI)

Then you have another group telling you to learn the newest language everyone is talking about, while companies are still hiring developers who know technologies that have been around for years.

So what is really worth learning in 2026?

Not the language with the loudest hype.

Not the one that gets the most viral posts.

The languages that matter are the ones powering the things companies are building right now: AI systems, cloud infrastructure, mobile apps, enterprise software, and high-performance applications.

After looking at where the industry is moving, these are the five programming languages I believe deserve your attention in 2026:

Python
Go
Rust
Zig
Java (plus Kotlin)

If you're looking for your first developer job or planning your next career move, these languages are among the best programming languages to learn in 2026.

Let's talk about why.

1. Python: Still the Language Behind the AI Revolution

Yes, Python again.

Some developers might roll their eyes because Python has been on every "languages to learn" list for years.

But there is a reason it keeps coming back.

Python is no longer just a beginner-friendly programming language. In 2026, it has become one of the main building blocks of the AI ecosystem.

Almost every major AI workflow involves Python somewhere:

Machine learning models
AI agent frameworks
Data processing pipelines
Automation scripts
Web scraping tools
LLM applications

If you are building something with artificial intelligence today, chances are you will eventually touch Python.

But here is the important part:

Knowing Python syntax alone is not impressive anymore.

Almost everyone can create a small Python project and upload it to GitHub.

The real value comes from what you build with it.

A developer who knows Python and understands AI systems, automation, backend development, or data workflows has a stronger advantage than someone who only knows basic syntax.

Python is not exciting because it is new.

It is exciting because it keeps becoming useful in new areas.

2. Go: The Language Running the Cloud

Go is probably not the language you see trending every week on social media.

There are no endless debates about Go syntax. There are fewer flashy tutorials compared to JavaScript or Python.

But behind the scenes, Go is everywhere.

A huge amount of modern cloud infrastructure depends on it.

If you have ever used technologies like containers, cloud platforms, or Kubernetes-based systems, you have probably interacted with software written in Go.

The reason is simple:

Go was designed for modern infrastructure.

It handles concurrency extremely well, making it a great choice for systems that need to process lots of tasks at the same time without turning into a maintenance nightmare.

Companies love it because it helps developers build:

Fast backend services
Cloud-native applications
Developer tools
Distributed systems

Go might not be the most exciting language to talk about.

But when companies need reliable systems that scale, Go keeps appearing.

And that is usually a good sign for job opportunities.

3. Rust: Performance Without Giving Up Safety

Rust is the language that many developers want to learn but hesitate to start.

And honestly, the reason is understandable.

Rust has a reputation for being difficult.

The ownership system and borrow checker can feel confusing when you first encounter them.

You might spend your first few weeks asking:

"Why is the compiler fighting me?"

But after that initial learning curve, Rust starts making more sense.

The biggest advantage of Rust is that it combines two things developers usually have to choose between:

Low-level performance
Memory safety

Traditionally, languages like C and C++ gave developers incredible control and speed, but that power also came with many risks.

Rust tries to provide that same level of performance while preventing many common memory-related problems before the program even runs.

That is why Rust is appearing in areas like:

Operating systems
Browser engines
Infrastructure tools
Security-focused applications
High-performance backend services

It is also becoming one of the most respected languages among developers.

Rust is not the easiest language to learn.

But for developers who want to work closer to the hardware or build extremely reliable systems, it is one of the most interesting choices in 2026.

4. Zig: The Newcomer Developers Should Watch

Now let's talk about the language that many developers are still discovering:

Zig.

Zig is not trying to replace every programming language.

Instead, it is targeting a specific problem:

How can we make low-level programming simpler and more enjoyable?

Zig takes inspiration from C but tries to remove some of its frustrations.

The idea is simple:

Give developers more control without adding unnecessary complexity.

One interesting feature is Zig's compile-time execution, which allows developers to run certain logic while compiling instead of waiting until the program runs.

This can make systems more efficient and predictable.

Zig still has a smaller ecosystem than languages like Python, Java, or Go.

But it has been gaining attention because developers are interested in alternatives for:

Systems programming
Embedded development
Performance-focused applications

It might not be the language you need to learn tomorrow.

But it is definitely one worth watching.

Sometimes the languages that quietly grow today become the technologies everyone talks about later.

5. Java and Kotlin: The Languages That Refuse to Disappear

Every time someone creates a "modern programming languages" list, someone asks:

"Java? Really?"

Java has been around for decades, but that does not mean it is outdated.

A huge amount of the world's software still depends on it.

Banks, insurance companies, large enterprises, and critical business systems continue running on Java because stability matters.

Companies don't rebuild massive systems from scratch just because a newer language becomes popular.

They choose technologies that are reliable, maintainable, and proven.

At the same time, Java's ecosystem has continued evolving.

For Android development, Kotlin has become the preferred language and is deeply integrated into modern Android workflows.

Kotlin also expanded beyond mobile with Kotlin Multiplatform, allowing developers to share code across different platforms.

So while Java represents stability, Kotlin represents a more modern direction.

Together, they remain extremely relevant in 2026.

So Which Programming Language Should You Learn in 2026?

It depends on what you want to build.

If you want to work in AI, automation, or data:

Start with Python.

If you want backend development and cloud infrastructure:

Learn Go.

If you want performance, security, and systems programming:

Learn Rust.

If you are interested in low-level programming and want to explore something newer:

Keep an eye on Zig.

If you want enterprise development or Android:

Java and Kotlin are still excellent choices.

There is no single "best" programming language.

The best language is the one that helps you build something useful.

Final Thoughts

The programming world changes quickly.

A language that looks unstoppable today can become irrelevant tomorrow, and a language that seems "boring" can quietly power some of the biggest systems in the world.

That is why choosing what to learn in 2026 is not about chasing every new trend. It is about understanding where technology is moving and picking the tools that match the problems you want to solve.

But you should always keep in mind that learning a programming language is only the beginning.

A developer who understands one language deeply and knows how to solve problems will always be more valuable than someone who knows ten languages only at a beginner level.

💬 Now I want to hear your opinion.

Which programming language do you think will matter the most in 2026?

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

24 Best AI Design Tools for Designers and Developers in 2026

Hadil Ben Abdallah — Thu, 30 Jul 2026 09:04:11 +0000

Not long ago, designers relied on AI mostly for generating images or experimenting with quick concepts. Today, AI design tools can generate complete user interfaces, create interactive applications, write production-ready code, build marketing assets, and even help developers move from a Figma file to a working product much faster than before.

That rapid evolution has also created a new problem.

Search for AI design tools, and you'll find hundreds of recommendations. And looking at feature lists alone doesn't tell you which one fits your requirements.

That's why I tested them myself.

Instead of creating another roundup based on documentation, I spent time using 24 popular AI design tools across different categories. Whenever tools shared the same purpose, I evaluated them using the same prompt. For workflow-focused platforms that solve different problems, I created individual prompts that reflected their primary use case.

The result is a practical guide for designers, developers, product managers, and founders who want to understand what each tool does and which one deserves a place in their workflow.

If you're searching for the best AI design tools, AI coding tools, AI website builders, or AI tools for designers and developers, this guide will help you choose the right platform.

TL;DR

After testing all 24 AI design tools, these are the platforms that stood out for different workflows:

Most complete AI UI design workflow: Flowstep → Multi-screen generation, editable canvas, React + TypeScript export, and MCP integration.
Fastest way to build an AI web app: Lovable → Quickly turns prompts into functional applications.
Strongest tool for marketing assets: Canva AI → Fast content creation with an easy editing workflow.
Highest-quality AI illustrations: Leonardo AI → Detailed concept art with excellent realism.
Most capable AI coding assistant: Cursor → Understands entire projects instead of individual files.

The best choice ultimately depends on your workflow.

What Are AI Design Tools?

AI design tools are software powered by artificial intelligence that help designers and developers create digital products more efficiently. Depending on the tool, AI can generate interfaces from text prompts, produce marketing graphics, create illustrations, write frontend code, build complete web applications, recommend color palettes, analyze user experiences, or automate repetitive design tasks.

One reason these platforms have become so popular is that they reduce the time spent on routine work. Instead of building every layout from scratch or manually writing boilerplate code, teams can start with AI-generated foundations and spend more time refining ideas and solving real product problems.

Today's AI design software generally falls into several categories.

AI UI Design & Code Export: Generating complete user interfaces that can be exported to Figma or converted into React components
AI App & Website Builders: Producing functional applications instead of static mockups.
AI Graphic Design & Visual Content: Creating branding assets
AI image generators: Producing original visuals
AI Coding Agents & Dev Tools: Assist developers during implementation
Design Workflow & UX Tools: Improve research, testing, and product planning.

These tools are not here to replace designers or developers; they are becoming productivity multipliers. They handle repetitive work, accelerate prototyping, and shorten the gap between an idea and a working product, allowing teams to iterate much faster.

How I Evaluated These AI Design Tools

Comparing AI design tools isn't as simple as asking each platform to generate a nice-looking screen.

These tools solve different problems, so judging all of them using the same expectations wouldn't produce a fair comparison.

Instead, I grouped the tools by category and evaluated each one according to the type of work it was designed to do. Whenever multiple tools shared the same purpose, I tested them using the same prompt without modifying the requirements. For categories where every platform serves a different role, such as UX research or workflow automation, I created prompts tailored to each tool's primary use case.

Throughout the testing process, I focused on the factors that matter most in real-world design and development workflows.

Evaluation Criteria

Output quality

Did the generated result look polished, realistic, and ready to build upon? I evaluated how professional the final output was.

Ease of use

Some AI tools produce useful results within minutes, while others require more setup or experience. I considered how quickly someone could go from opening the platform to generating a meaningful result.

Developer friendliness

For tools that generate code or applications, I paid attention to code quality, framework support, export options, maintainability, and how practical the generated project would be for continued development.

Design consistency

Whenever a tool generated multiple screens or visual assets, I checked whether typography, colors, spacing, components, and branding remained consistent across the entire project.

Pricing and value

Free plans, pricing structure, and overall value were also important. Some tools justify premium pricing through advanced capabilities, while others offer generous free tiers.

By evaluating every platform through these criteria, the comparison focuses on how these AI design tools perform during real projects.

Quick Comparison Table

Before exploring each tool in detail, this comparison table provides a quick overview of the 24 best AI design tools in 2026. It compares AI UI design tools, AI website builders, AI graphic design tools, AI image generators, AI coding tools, and AI UX tools based on their category, ideal use case, free availability, starting price, and standout capabilities to help designers and developers quickly find the right AI design software for their workflow.

Tool	Category	Best For	Free Plan	Starting Price	Standout Feature
Flowstep	AI UI Design & Code Export	Product designers & frontend teams	✅	$15/mo	Multi-screen UI generation + React export + MCP
Google Stitch	AI UI Design & Code Export	Design systems	✅	Free	Automatic design system generation
Figma Make	AI UI Design & Code Export	Figma users	✅	Included with Figma AI	Interactive apps inside Figma
Lovable	AI App Builder	Startup founders	✅	$25/mo	Full-stack app generation
Bolt.new	AI App Builder	Rapid prototyping	✅	$25/mo	Prompt-to-app workflow
Replit	AI App Builder	Developers	✅	$20/mo	Cloud IDE with AI
v0	AI App Builder	Frontend developers	✅	$30/mo	Production-ready React components
Framer	AI Website Builder	Marketing websites	✅	$10/mo	AI website publishing
Canva AI	Graphic Design	Marketing teams	✅	$6.50/mo	Magic Design
Adobe Firefly	Graphic Design	Creative professionals	✅	$9.99/mo	Commercial-safe AI generation
Microsoft Designer	Graphic Design	Beginners	✅	Free	Microsoft ecosystem integration
Kittl AI	Graphic Design	Branding & print	✅	$15/mo	Typography-first design
Playground AI	AI Image Generator	Creators	✅	$15/mo	Built-in image editing
Ideogram	AI Image Generator	Typography images	✅	$20/mo	Best AI text rendering
Leonardo AI	AI Image Generator	Professional concept art	✅	$12/mo	High-quality visual assets
Stable Diffusion	AI Image Generator	Advanced users	✅	Free / Credits	Fully customizable open-source models
Cursor	AI Coding Tool	Developers	✅	$20/mo	Codebase-aware AI agent
Cline	AI Coding Tool	Autonomous coding	✅	API cost	Multi-step coding agent
Devin Desktop	AI Coding Tool	Engineering teams	✅	$20/mo	Autonomous software engineer
GitHub Copilot	AI Coding Tool	Everyday coding	✅	$10/mo	AI pair programmer
Khroma	Design Workflow & UX	Color systems	✅	Free	Personalized palettes
Fontjoy	Design Workflow & UX	Typography	✅	Free	AI font pairing
Webflow	Design Workflow & UX	Website publishing	✅	$25/mo	Visual CMS & hosting
Miro AI	Design Workflow & UX	Product planning	✅	$10/mo	AI whiteboards

The 24 AI design tools below are grouped by category so you can compare platforms that solve similar problems. Instead of ranking completely different products against each other, this approach makes it easier to choose the right tool based on your workflow

AI UI Design & Code Export Tools

AI UI design tools have evolved far beyond generating attractive mockups. The best platforms can now create complete multi-screen experiences, maintain a consistent design system, export production-ready code, and integrate directly into modern development workflows.

For designers, this means spending less time rebuilding repetitive layouts and focusing more on refining user experiences. For developers, it shortens the journey from an idea to working code by generating reusable components and structured frontend foundations.

To make this comparison fair, I tested every tool in this category using the same prompt.

Prompt Used

Design a modern personal finance and budgeting platform called BudgetFlow.

Generate these four screens:

• Financial Dashboard
• Transactions
• Budgets
• Savings Goals

Requirements:

• Modern fintech UI
• Light theme with subtle gradients
• Charts and analytics
• Responsive layout
• Search and filtering
• Transaction categories
• Progress indicators
• Reusable design system
• Consistent navigation
• Accessible color contrast

1. Flowstep

Flowstep is one of the most advanced AI UI design tools for turning text prompts into production-ready interfaces. After using it, I noticed that it doesn't only focus on creating attractive screens; it tries to close the gap between design and development from the beginning.

The platform can generate multiple connected screens from a single prompt, accepts references such as images, URLs, or design documents, supports manual editing alongside AI generation, and exports clean React, TypeScript, and Tailwind CSS code. It also integrates with MCP-compatible AI coding tools, making it an appealing option for teams that want to move quickly from concept to implementation.

What it generated

For the BudgetFlow prompt, Flowstep generated a complete fintech interface that felt like a real product instead of a collection of unrelated screens. The dashboard, transactions page, budgets view, and savings goals all shared the same visual language, with consistent navigation, spacing, typography, and reusable components.

I also liked how well it organized financial data. Charts, transaction tables, budget cards, and progress indicators all looked natural, giving the interface a polished feel that looked ready to move into a design workflow.

Best for

UI/UX designers, product designers, startups, and development teams looking for a smooth transition from AI-generated designs to production-ready frontend code.

Key features

Multi-screen UI generation
AI and manual editing on the same canvas
React, TypeScript, and Tailwind CSS export
Copy directly into Figma
Reference-based generation
MCP integration
Auto Layout support
Collaborative editing

Pricing

Free plan available, with paid plans starting at $15/month with message-based pricing, collaboration features, and advanced capabilities.

2. Google Stitch

Google Stitch approaches AI UI generation differently from most competitors. Instead of only creating interfaces, it places a strong emphasis on building structured design systems alongside the generated screens.

Powered by Google's Gemini models, Stitch generates interfaces together with reusable colors, typography, spacing, and component styles. It also offers several export options that allow teams to continue working inside their preferred design or development tools.

What it generated

Google Stitch produced a clean and modern budgeting application that looked balanced. The four requested screens maintained strong consistency throughout the project, and the automatically generated design system made the output feel more structured.

The dashboards handled financial information well, with readable charts, organized transaction lists, and a clear visual hierarchy that made the interface clean.

Best for

Product designers, design teams, and organizations that prioritize design systems and structured UI foundations before moving into development.

Key features

Prompt-based UI generation
Automatic design system creation
Design tokens
Responsive layouts
Gemini-powered generation
HTML export
Export to Figma and other supported platforms
Reusable component generation

Pricing

Free to use through Google Labs with monthly generation limits.

3. Figma Make

Figma Make extends the familiar Figma workflow by introducing AI-powered application generation directly inside the platform many designers already use every day. It produces interactive experiences that can continue evolving through conversational prompts.

Because everything happens inside Figma, teams can move naturally between AI generation, collaborative editing, prototyping, and developer handoff without constantly switching between different applications.

What it generated

Figma Make generated a cohesive BudgetFlow interface with all four requested screens while maintaining strong consistency across typography, spacing, colors, and reusable UI components.

During testing the workflow was very comfortable. It wasn't treating AI as a separate tool; it was like a natural extension of the existing Figma experience. Being able to refine the generated interface through conversation while staying inside the same workspace made iteration enjoyable.

Best for

Design teams already working inside Figma who want to accelerate ideation, prototyping, and collaborative product design using AI.

Key features

AI-powered application generation
Native Figma workflow
Conversational refinement
Interactive prototypes
Editable design files
TypeScript export
Team collaboration
Design reasoning during generation

Pricing

Included with supported Figma AI features, with availability depending on your Figma plan.

AI App & Website Builders

AI app builders have changed what "prototyping" means.

A few years ago, turning an idea into a working application required designing every screen, building the frontend, connecting the backend, and wiring everything together before anyone could interact with the product. Today's AI app builders shorten that process. Many of them can generate complete applications from a single prompt, including navigation, authentication, responsive layouts, and editable source code.

That doesn't mean every platform serves the same audience. Some prioritize developer control, while others focus on helping founders validate ideas quickly or enabling designers to build functional products without writing much code.

To compare them fairly, I gave every tool in this category the same prompt and evaluated how well it handled the overall application, the user experience, code quality, navigation, and developer workflow.

Prompt Used

Build a complete event management web application called EventSphere.

Include:

• Landing Page
• Event Discovery
• Ticket Checkout
• User Dashboard

Requirements:

• Working navigation
• Interactive booking flow
• Responsive layout
• Authentication
• Modern UI
• Dummy event data
• Search and filters
• Functional components

4. Lovable

Lovable has quickly become one of the most popular AI app builders because it focuses on something many developers and founders care about: turning an idea into a working product with as little friction as possible. It creates complete applications that can continue evolving through conversation.

Its biggest strength is how it combines interface generation, application logic, and iterative development into a single workflow. Instead of starting from a blank project, you begin with a functional application that you can refine as you want.

What it generated

Lovable produced a complete EventSphere application with all four requested pages connected through a coherent user flow. The landing page introduced upcoming events, the discovery page supported searching and filtering, the checkout process was structured, and the user dashboard provided a realistic overview of purchased tickets and upcoming events.

What impressed me most was that the application was complete from the first generation. Instead of producing disconnected pages, it generated a product that you could already continue building into an MVP.

Best for

Startup founders, product teams, developers, and anyone who wants to validate ideas by generating functional web applications quickly.

Key features

Full-stack application generation
Natural language development
Interactive editing through chat
Authentication support
Backend integration
Responsive layouts
Rapid MVP creation
One-click deployment options

Pricing

Free plan available, with paid plans offering additional usage, faster generation, and advanced project capabilities.

5. Bolt.new

Bolt.new is between an AI coding assistant and an AI software engineer. It doesn't stop after generating interface mockups; it creates a working application that developers can inspect, modify, and extend immediately.

Bolt has become popular thanks to its conversational workflow. Every iteration feels like collaborating with another developer.

What it generated

Bolt generated the complete EventSphere application with all four requested pages while maintaining a consistent visual identity throughout the project. Navigation between pages worked well, and the generated interface was clean and functional.

Compared with some other tools, the visuals leaned toward simplicity rather than elaborate design, but the generated application provided a solid technical foundation that developers could continue building.

Best for

Developers looking for fast AI-assisted web application development and iterative product building.

Key features

Prompt-to-app generation
Live development environment
React project generation
Conversational editing
Source code export
Deployment integrations
Rapid application iteration
Modern frontend workflows

Pricing

Free plan available with usage limits, alongside paid plans for higher limits and premium features.

6. Replit

Replit approaches AI-generated applications from a developer-first perspective. It focuses on generating projects that can immediately be executed, edited, tested, and deployed inside its cloud-based development environment.

For developers who prefer working directly with source code while still benefiting from AI-assisted generation, that workflow is a good choice for them.

What it generated

Replit generated a fully functional EventSphere application with all requested pages connected through working navigation. The generated project included realistic event listings, structured booking flows, responsive layouts, and editable source code that could immediately be expanded.

During testing I really enjoyed the visual quality of the generated interface. Replit produced a polished layout with well-balanced colors, clear typography, and a clean overall structure.

Best for

Developers who want AI-generated applications inside a complete cloud development environment.

Key features

AI full-stack generation
Browser-based IDE
Live preview
Integrated deployment
AI code editing
Collaborative development
Source code access
Cloud hosting

Pricing

Free plan available, with paid subscriptions unlocking more AI usage, private projects, and advanced development features.

7. v0 by Vercel

Although v0 is often associated with frontend component generation, it has evolved into a capable AI application builder for modern React ecosystems. Instead of producing static designs, it generates structured React interfaces that fit into existing frontend projects.

Developers working with React, Next.js, Tailwind CSS, and modern component libraries will likely find its workflow immediately familiar.

What it generated

v0 generated a modern EventSphere interface with clean page layouts, reusable components, responsive navigation, and structured frontend architecture.

The generated project emphasized component quality over visual experimentation. The resulting interface looked easy to extend and aligned with current frontend development practices.

Best for

Frontend developers building React and Next.js applications who want production-ready AI-generated components.

Key features

React generation
TypeScript support
Tailwind CSS integration
Component architecture
Responsive layouts
Chat-based refinement
Modern frontend patterns
Easy project integration

Pricing

Free plan available, with Pro plans offering additional generations, higher limits, and premium AI capabilities.

8. Framer

Framer has evolved from a prototyping platform into one of the strongest AI website builders available today. Instead of generating wireframes, it creates complete responsive websites that can be published with minimal additional work.

Its AI generation feels polished for marketing websites, landing pages, and product-focused experiences where visual presentation is just as important as functionality.

What it generated

Framer generated a polished EventSphere website with an attractive landing page, event listings, booking sections, and a user dashboard that maintained consistent styling throughout the experience.

The layouts looked modern, spacing was balanced, and the generated website required little cleanup. While it isn't intended to replace full-stack development platforms, it performed extremely well for creating production-quality marketing experiences.

Best for

Designers, marketers, agencies, and startups building modern websites and landing pages without starting from scratch.

Key features

AI website generation
Responsive layouts
Interactive prototypes
Built-in CMS
Hosting
SEO optimization
Visual editing
Publishing workflow

Pricing

Free plan available, with paid plans unlocking custom domains, CMS features, advanced publishing, and team collaboration.

AI Graphic Design & Visual Content Tools

AI graphic design tools help designers, marketers, and content creators produce polished visual assets much faster than traditional design workflows. These tools specialize in creating marketing materials, social media graphics, presentations, branding assets, and commercial-ready visuals that can be customized for different campaigns.

As these AI design tools continue to improve, they are becoming valuable assistants. They accelerate repetitive tasks, generate creative concepts, and help teams maintain consistent branding across multiple channels without stealing human creativity and refinement.

To compare these tools fairly, I used the same branding challenge for every platform and evaluated how well each one handled visual consistency, typography, layout quality, brand identity, and overall presentation.

Prompt Used

Create a LinkedIn banner for a sustainable travel company called EcoVoyage.

Requirements:

• Premium branding
• Nature-inspired colors
• Consistent typography
• Modern layouts
• Commercial-ready visuals
• Cohesive brand identity

9. Canva AI

Canva AI has moved from a beginner-friendly design platform into one of the best AI graphic design tools available today. Its AI features help users generate layouts, edit images, write marketing copy, and build complete visual campaigns while keeping everything inside Canva's familiar drag-and-drop editor.

One of Canva AI's biggest strengths is accessibility. Designers, marketers, entrepreneurs, and content creators can move from an idea to a polished design without switching between multiple applications. Combined with thousands of templates and brand management features, it has become a practical solution for everyday design work.

What it generated

For the EcoVoyage branding challenge, Canva AI produced 4 professional LinkedIn banners featuring a clean layout, balanced typography, and nature-inspired visuals. The overall design communicated the sustainable travel theme.

The typography hierarchy, spacing, and color palette were solid, but the generated banner required only minor adjustments before being presentation-ready. It needs to be resized because it doesn't have the best ratio for a LinkedIn banner.

Best for

Marketing teams, content creators, small businesses, freelancers, and designers creating social media graphics and branded marketing assets.

Key Features

AI-powered design generation
Magic Design
Brand Kit integration
AI image editing
Large template library
Social media content creation

Pricing

Free plan available. Canva Pro offers additional generations and higher limits.

10. Adobe Firefly

Adobe Firefly is Adobe's generative AI platform built specifically for creative professionals. Unlike many AI image generators, Firefly focuses on commercially safe content and integrates directly into Adobe Creative Cloud applications such as Photoshop, Illustrator, and Express.

Adobe Firefly is not here to replace professional design workflows; it enhances them by helping designers generate images, backgrounds, text effects, and creative assets while maintaining compatibility with industry-standard design tools.

What it generated

For the EcoVoyage campaign, Adobe Firefly generated a polished LinkedIn banner with premium-looking visuals, harmonious color choices, and a refined layout. The generated imagery reflected the sustainable travel concept while maintaining a realistic and professional appearance.

The overall design was well suited for commercial branding projects. The image quality, typography placement, and visual balance are good, making Firefly a strong choice for teams already working within the Adobe ecosystem.

Best for

Professional designers, creative agencies, enterprise marketing teams, and businesses already using Adobe Creative Cloud.

Key Features

Commercially safe AI generation
Photoshop integration
Illustrator integration
Generative Fill
AI text effects
Brand-safe creative workflows

Pricing

Free plan with limited AI credits. Premium features are included with eligible Adobe Creative Cloud subscriptions.

11. Microsoft Designer

Microsoft Designer combines AI-powered graphic design with Microsoft's productivity ecosystem, making it easy to create banners, presentations, advertisements, and social media graphics without requiring advanced design skills. Powered by Microsoft's AI technologies, it emphasizes simplicity and speed.

For users already working with Microsoft 365, Designer fits naturally into existing workflows by allowing quick creation of branded visuals that can be shared across presentations, documents, and marketing campaigns.

What it generated

For the EcoVoyage branding task, Microsoft Designer produced 4 modern LinkedIn banners featuring clean layouts, attractive nature-themed imagery, and balanced typography. The overall design captured the requested branding direction while keeping the layout simple and easy to customize.

The banner size was the required size for a LinkedIn banner, and the typography hierarchy, spacing, and color palette were cohesive, making it one of the fastest tools for producing marketing graphics.

Best for

Microsoft 365 users, marketers, entrepreneurs, educators, and businesses creating everyday marketing materials.

Key Features

AI graphic design generation
Microsoft 365 integration
Text-to-design
AI image generation
Brand templates
Social media design tools

Pricing

Free plan available with a Microsoft account. Premium capabilities are included with select Microsoft 365 subscriptions.

12. Kittl AI

Kittl AI focuses on high-quality graphic design with a strong emphasis on branding, typography, print design, and marketing assets. Unlike general-purpose AI design platforms, Kittl combines AI generation with powerful editing tools that allow designers to refine every visual element before exporting the final design.

It excels at producing polished branding materials, promotional graphics, merchandise designs, posters, and social media assets while giving users more creative control.

What it generated

For the EcoVoyage branding campaign, Kittl AI created a premium LinkedIn banner with elegant typography, sophisticated composition, and a solid visual identity centered around sustainable travel. The design was polished and crafted.

The combination of modern layouts, carefully selected colors, and strong typography made it one of the most visually refined outputs in this category. But it needs to be resized because it doesn't have the best ratio for a LinkedIn banner.

Best for

Brand designers, marketing professionals, creative agencies, print designers, and businesses building premium visual identities.

Key Features

AI-powered graphic design
Advanced typography tools
Brand asset creation
Poster and social media design
Illustration generation
Professional editing controls

Pricing

Free plan available. Paid plans unlock premium templates, higher export quality, and advanced AI features.

AI Image Generation Tools

AI image generators solve a different problem compared with AI UI design tools, AI website builders, or AI coding tools. Instead of creating complete interfaces or functional applications, they focus on transforming text descriptions into original visual assets.

These tools have become essential for designers, marketers, and creative teams who need illustrations, concept art, product visuals, marketing graphics, and brand assets without starting every project from a blank canvas. Modern AI image generators can now understand complex visual instructions, maintain consistent styles, and produce professional-quality images suitable for real-world projects.

For this category, I tested each tool using the same prompt to compare how different AI models interpret the same creative direction, including image quality, composition, lighting, realism, and overall consistency.

Prompt Used

Create a cinematic illustration of a futuristic software engineer working inside a neon-lit workspace.

Include:

• multiple monitors
• holographic UI
• realistic lighting
• reflections
• depth of field
• subtle cyberpunk atmosphere

Style:

• Highly detailed
• Professional concept art
• Commercial quality
• 4K

13. Playground AI

Playground AI is an accessible AI image generator designed for creators who want to generate and refine visuals without dealing with complex workflows. It combines text-to-image generation with editing capabilities, allowing users to create concepts, modify existing images, and experiment with different styles from one workspace.

Compared with purely prompt-based AI image tools, Playground AI focuses more on creative exploration. It gives designers and content creators an easier way to iterate on ideas, making it useful for social media graphics, marketing visuals, illustrations, and early-stage design concepts.

What it generated

For this test, Playground AI generated a futuristic software engineer workspace with the requested cyberpunk atmosphere, multiple monitors, and glowing holographic interfaces. The overall composition followed the prompt well, with strong lighting effects.

The final result captured the general mood of professional concept art, and the image quality was strong enough for creative exploration and visual inspiration.

Best for

Designers, marketers, content creators, and professionals looking for an easy-to-use AI image generator with built-in editing features.

Key Features

Text-to-image generation
AI image editing and variations
Canvas-based creative workflow
Multiple AI image models
Image enhancement tools
Fast visual experimentation

Pricing

Free plan available. Paid plans provide higher generation limits, faster processing, and access to additional features.

14. Ideogram

Ideogram is one of the most popular AI image generators for creating visuals where typography and text accuracy matter. While many AI image tools struggle with generating readable text inside images, Ideogram is a strong option for posters, advertisements, logos, branding concepts, and social media graphics.

The platform focuses on combining creative image generation with better text understanding. This makes it useful for designers who need AI-generated visuals that include headlines, product names, or branded elements.

What it generated

For the futuristic software engineer prompt, Ideogram produced 4 highly polished concept illustrations with strong composition and attractive lighting. The generated environment included detailed screens, futuristic interfaces, and a cinematic atmosphere that closely matched the requested direction.

The result was closer to professional concept art. The lighting, depth of field, and overall visual balance were among the strongest aspects.

Best for

Brand designers, marketing teams, creative agencies, and professionals creating posters, advertisements, and visual campaigns.

Key Features

Advanced text rendering in AI images
High-quality image generation
Multiple artistic styles
Prompt-based image refinement
Branding and marketing visual creation
Image variations and editing

Pricing

Free plan available with limited generations. Paid plans increase generation limits, processing speed, and access to premium features.

15. Leonardo AI

Leonardo AI is a powerful AI image generation platform focused on producing detailed, production-ready visuals. It is widely used by designers, creative teams, game developers, and marketers who need more control over image styles, characters, and visual consistency.

Unlike simpler AI image generators, Leonardo AI provides more customization options through different models, image controls, and editing features. This makes it suitable for users who want to move beyond basic prompt generation and create assets for professional projects.

What it generated

Leonardo AI generated one of the most detailed images in this comparison. The futuristic workspace included realistic lighting, reflections, multiple displays, and a strong sense of depth that matched the requirements of the prompt.

The final output looked close to professional concept art, with impressive environmental details and cinematic quality. The tool handled the futuristic atmosphere very well, creating a scene that could work for marketing visuals, presentations, or creative inspiration.

Best for

Professional designers, creative agencies, game developers, and teams creating high-quality visual assets.

Key Features

High-quality AI image generation
Custom AI models
Image editing and refinement
Style customization
Asset creation workflows
Advanced generation controls

Pricing

Free plan available with daily credits. Paid plans provide additional generations, higher limits, and advanced features.

16. Stable Diffusion

Stable Diffusion stands apart from many AI image generators because it is an open-source AI model. It gives developers and advanced creators the ability to run models locally, customize workflows, fine-tune outputs, and build highly specialized image-generation systems.

This flexibility has made Stable Diffusion popular among technical users who want maximum control over AI-generated visuals. Stable Diffusion prioritizes customization, making it a powerful option for users comfortable with more advanced workflows.

What it generated

For this experiment, Stable Diffusion generated 2 images, and it successfully captured the requested futuristic software engineer concept, including the neon workspace, holographic interfaces, multiple monitors, and cinematic lighting.

The quality of the final image depended heavily on model selection and prompt adjustments. With the right configuration, Stable Diffusion can produce extremely detailed results, but achieving the best output usually requires more experimentation compared with beginner-friendly AI image generators.

Best for

Advanced designers, AI enthusiasts, developers, and creative professionals who want maximum control over AI-generated images.

Key Features

Open-source AI image models
Local deployment options
Custom model support
Fine-tuning capabilities
Advanced image control
Community-created workflows

Pricing

Free when self-hosted. Cloud-based Stable Diffusion services usually use credit-based pricing models.

AI Coding Agents & Developer Tools

Modern AI coding agents can understand entire codebases, generate components, explain implementation decisions, refactor complex logic, and help developers move from ideas to working software faster.

Unlike traditional coding assistants that only suggest individual lines of code, AI tools for developers now work as collaborative engineering partners. They can analyze project context, modify multiple files, debug issues, and assist with complete development workflows.

For this category, I focused on a real frontend development case. I tested how each AI coding tool handles component architecture, code quality, accessibility requirements, and its ability to explain technical decisions.

Every tool received the same development challenge.

Prompt Used

Build a reusable React component library for a healthcare appointment platform.

Include:

• Appointment Card
• Doctor Profile Card
• Calendar Component
• Search Component
• Notification Toast
• Loading Skeleton

Requirements:

• TypeScript
• Tailwind CSS
• Accessibility (WCAG)
• Dark mode
• Responsive design
• Clean architecture
• Reusable components
• Explain implementation decisions

17. Cursor

Cursor has become one of the most popular AI coding tools because it combines a familiar code editor experience with powerful AI agents. Built on top of the VS Code ecosystem, it understands project context and helps developers generate features, refactor code, debug problems, and navigate large applications.

Cursor focuses on understanding the relationship between files, components, and application architecture. This makes it valuable for frontend developers working on React applications, design systems, and production codebases.

What it generated

For this healthcare component library task, Cursor generated a structured React project with reusable components, TypeScript types, and Tailwind CSS styling. The generated components followed common frontend patterns, making the code easier to maintain and extend.

One of the strongest parts of the output was the explanation of implementation decisions. Cursor did not only generate code but also explained choices around component structure, accessibility considerations, and reusability, which makes it useful for developers learning from AI-generated solutions.

Best for

Frontend developers, full-stack developers, software engineers, and teams building modern web applications.

Key Features

AI coding agent with project understanding
Multi-file code generation and editing
Code explanation and debugging
Natural language development workflow
VS Code-compatible environment
Context-aware suggestions

Pricing

Free plan available. Paid plans provide higher usage limits, larger context windows, and additional AI capabilities.

18. Cline

Cline is an open-source AI coding agent designed for developers who want a more autonomous development workflow. It can analyze tasks, create implementation plans, edit files, run commands, and iterate through development steps.

Its biggest difference is the agent-based approach. Developers can provide a high-level goal, and Cline attempts to complete multiple stages of the task while keeping track of the project context.

What it generated

For the React component library challenge, Cline created a clean component structure with reusable UI elements and logical file organization. It handled the TypeScript and Tailwind CSS requirements effectively while maintaining consistency between different components.

The generated solution also demonstrated strong awareness of accessibility requirements. Components included reusable patterns that could be adapted for production applications, although developers would still need to review and refine the implementation before shipping.

Best for

Developers working on larger applications who want an autonomous AI coding assistant capable of handling multi-step engineering tasks.

Key Features

Autonomous coding agent
Project-wide code understanding
File creation and modification
Terminal command execution
Multi-step development workflows
Open-source architecture

Pricing

Open-source and free to use. Costs depend on the AI model connected to Cline.

19. Devin Desktop

Devin Desktop represents a more advanced direction for AI coding tools: an AI software engineer capable of handling longer development tasks with less continuous guidance. It aims to manage complete engineering workflows from planning to implementation.

The platform is designed around the idea of AI agents working alongside developers. It can analyze requirements, write code, review changes, and help automate repetitive software development activities.

What it generated

For this test, Devin Desktop generated a complete React component library structure with reusable healthcare UI components. The output included TypeScript definitions, component organization, and styling approaches aligned with modern frontend development practices.

The generated implementation explanations were useful because they provided context behind architectural decisions. While AI-generated code still requires developer review, Devin Desktop showed how AI agents can support larger software engineering workflows.

Best for

Engineering teams automating complex development tasks and organizations exploring AI-assisted software engineering workflows.

Key Features

Autonomous software engineering
Long-running development tasks
Code generation and modification
Project analysis
AI-assisted development workflows
Engineering task automation

Pricing

Free plan available. Paid plans provide higher usage limits.

20. GitHub Copilot

GitHub Copilot remains one of the most widely adopted AI coding assistants because it integrates directly into the workflows developers already use. It enhances traditional development practices by providing code suggestions, explanations, debugging assistance, and productivity improvements.

As part of the GitHub ecosystem, Copilot benefits from deep integration with popular development environments. It is particularly useful for developers who want AI assistance while maintaining full control over implementation decisions.

What it generated

For the healthcare component library challenge, GitHub Copilot generated clean React components using TypeScript and Tailwind CSS. But the output was very basic, and the loading skeleton was almost empty, but it could be a good starting point for building reusable UI elements.

Compared with more autonomous AI coding agents, Copilot required more developer direction during implementation.

Best for

Frontend developers, software engineers, and teams already using Visual Studio Code or GitHub-based workflows.

Key Features

AI code completion
Coding chat assistant
Code explanations
Debugging support
Multi-language programming support
IDE integration

Pricing

Free plan available. Paid plans provide additional AI features, higher usage limits, and advanced capabilities.

Design Workflow & UX Tools

Not every AI design tool is built to generate interfaces, write code, or create visual assets. Some tools focus on improving the overall design workflow by helping teams understand users, build stronger design systems, organize ideas, and make better product decisions.

These AI UX tools support different stages of the product design process, from user research and usability testing to typography selection, color exploration, website creation, and team collaboration. They are especially valuable for product designers and teams that want to combine AI automation with a more structured design workflow.

Because each tool in this category solves a different problem, I did not use the same prompt. Instead, I created a task that matches the main purpose of each tool and evaluated how effectively it supports real design workflows.

21. Khroma

Khroma is an AI color palette generator designed to help designers discover color combinations based on their preferences and project requirements. Instead of manually searching through thousands of color combinations, designers can use AI recommendations to quickly explore suitable palettes.

The tool is useful during branding and UI design stages, where choosing the right colors can significantly impact usability, accessibility, and product identity.

Prompt Used

Generate a modern color palette for Aurora Learning Platform.

The brand should feel:

• Professional
• Trustworthy
• Educational
• Modern
• Accessible

What it generated

For the Aurora Learning Platform branding task, Khroma generated several color combinations that matched the requested direction. The suggested palettes balanced professional and educational qualities with a modern visual style.

The generated options were useful as a starting point for building a design system. While designers would still need to validate colors against accessibility standards and brand guidelines, Khroma helped reduce the time spent exploring initial color directions.

Best for

UI designers, brand designers, product teams, and anyone building visual design systems.

Key Features

AI-generated color palettes
Personalized color recommendations
Brand inspiration
Color combination exploration
HEX and RGB values
Visual palette previews

Pricing

Free.

22. Fontjoy

Fontjoy is an AI typography tool that helps designers discover font combinations that work well together. Typography plays an important role in user experience, especially for digital products where readability and visual hierarchy directly affect how users consume information.

Instead of manually testing hundreds of font combinations, Fontjoy uses machine learning to suggest pairings based on contrast, similarity, and overall design balance.

Prompt Used

Generate font pairings for Aurora Learning Platform.

Requirements:

• Readable
• Modern
• Accessible
• Suitable for dashboards
• Suitable for marketing pages

What it generated

For the Aurora Learning Platform typography task, Fontjoy suggested several font combinations that balanced modern aesthetics with readability. The generated pairings were suitable for both marketing pages and dashboard interfaces.

The results provided a useful starting point for defining a product’s typography system. While final font choices would depend on brand identity and accessibility testing, Fontjoy made the exploration process faster and more structured.

Best for

UI designers, brand designers, frontend developers, and marketing teams creating digital products.

Key Features

AI font pairing recommendations
Typography exploration
Font compatibility analysis
Design inspiration
Readability-focused suggestions

Pricing

Free.

23. Webflow

Webflow combines visual website building with powerful publishing capabilities, allowing designers to create responsive websites without writing every line of code. It is between traditional website builders and professional development workflows, giving designers visual control while producing websites that are ready to publish.

For designers, freelancers, and marketing teams, Webflow is useful for creating landing pages, marketing websites, and interactive experiences where visual quality and customization matter.

Prompt Used

Build the marketing landing page for Aurora Learning Platform.

Include:

• Hero
• Features
• Testimonials
• Pricing
• CTA
• Responsive layout

What it generated

For the Aurora Learning Platform landing page task, Webflow generated a complete website structure containing the main sections requested: hero area, feature highlights, testimonials, pricing, and calls to action.

The generated layout followed common SaaS website patterns with responsive design considerations. While further customization would be needed for a final production website, the output provided a strong foundation for quickly moving from idea to a functional marketing page.

Best for

Web designers, freelancers, agencies, startups, and marketing teams building professional websites.

Key Features

Visual website builder
Responsive design controls
CMS capabilities
SEO tools
Website publishing
Custom interactions and animations

Pricing

Free workspace available. Paid plans are required for advanced website hosting and additional features.

24. Miro AI

Miro AI focuses on collaboration, brainstorming, and product planning. It brings AI assistance into digital whiteboards, helping teams organize ideas, document workflows, and structure product discussions.

For UX teams and product managers, Miro AI can accelerate early-stage planning by turning simple descriptions into structured boards that teams can refine collaboratively.

Prompt Used

Generate a product planning board for Aurora Learning Platform.

Include:

• User personas
• User journey
• Feature prioritization
• Roadmap
• Brainstorm ideas

What it generated

For the product planning task, Miro AI created a structured workspace containing user personas, journey mapping, feature prioritization ideas, roadmap sections, and brainstorming notes.

The generated board was useful as a starting point for product workshops and team discussions. Miro AI works as a productivity layer that helps teams organize information faster and collaborate more effectively.

Best for

Product managers, UX teams, design teams, and organizations running collaborative planning sessions.

Key Features

AI brainstorming assistance
Product planning boards
User journey mapping
Collaborative whiteboards
Workshop organization
Team collaboration tools

Pricing

Free plan available. Paid plans provide additional collaboration features, larger boards, and enterprise capabilities.

Which AI Design Tools Should You Choose?

After testing all 24 AI design tools, I can say that there isn't a single "best" AI design tool. Every platform is designed to solve a different problem across the design and development workflow.

Some tools help you explore ideas faster. Others generate production-ready code. Some are built for branding and visual content, and others focus on UX research or developer productivity.

A better question is:

Which AI design tool solves the problem you're trying to solve today?

Here's where I think each category shines.

If you're designing UI for web or mobile applications

If your workflow starts with product interfaces, AI UI design tools will save you the most time.

Flowstep impressed me the most for complete product flows because it combines multi-screen generation with editable designs, React code export, and developer-friendly workflows.

If your goal is building complete applications

Sometimes you don't need mockups; you need a working product.

That's where AI app builders become much more valuable than traditional design tools.

Lovable goes beyond interface generation by producing functional applications that developers can continue building immediately.

This tool significantly reduces the distance between an idea and a working prototype.

If you're creating marketing assets or brand content

Design teams don't only build software.

They also create banners, advertisements, presentations, landing pages, and social media graphics.

Canva AI remains one of the easiest AI graphic design tools for creating banners, presentations, social media graphics, and other marketing assets in minutes.

If you need high-quality AI-generated images

For concept art, blog illustrations, product visuals, presentations, and creative assets, dedicated AI image generators still produce the best results.

Leonardo AI produced some of the most detailed and realistic images during my testing, making it an excellent choice for concept art and professional creative work.

If you're a frontend developer

Developer workflows have changed over the past year.

Instead of writing every component manually, AI coding agents can now generate reusable React components, explain implementation decisions, refactor projects, and even understand entire codebases.

Cursor stood out because it understands entire codebases instead of isolated files, making it feel like working with another experienced developer.

If your work focuses on UX and product strategy

Not every design decision starts with pixels.

Sometimes you need better research, better planning, or stronger design systems.

Khroma helps build stronger color systems, Fontjoy simplifies typography selection, Webflow speeds up website creation, and Miro AI makes brainstorming and product planning more organized.

These tools don't replace designers; they help designers make better decisions.

Frequently Asked Questions

What are the best AI design tools in 2026?

→ The best AI design tools depend on what you're building. For UI and product design, Flowstep is an excellent choice. For AI app development, Lovable stands out. Canva AI is a strong option for graphic design, while Cursor is one of the best AI coding tools for developers.

Which AI design tool is best for UI/UX designers?

→ If your work focuses on digital products, Flowstep, Google Stitch, and Figma Make are among the best AI UI design tools available today. They can generate complete interfaces, maintain consistent design systems, and integrate smoothly with modern development workflows.

Can AI design tools generate production-ready code?

→ Yes, but not all of them.

Some AI design tools focus only on interface generation, while others produce reusable code alongside the design.

Platforms like Flowstep, v0 by Vercel, Replit, Bolt.new, and Lovable can generate code that developers can continue building on. AI coding tools such as Cursor, Cline, Devin Desktop, and GitHub Copilot further accelerate implementation by generating, refactoring, and explaining code.

Should designers use multiple AI tools instead of just one?

→ In most cases, yes.

No single AI design tool excels at every task. Many designers combine several specialized tools depending on the project. For example, you might use Flowstep for interface design, Leonardo AI for illustrations, Canva AI for marketing assets, and Cursor to accelerate frontend development. Building a small AI toolkit usually produces better results than relying on one platform for everything.

Final Thoughts

When I started testing these AI design tools, I expected to compare interface quality. But I ended up comparing completely different ways of building digital products.

Some platforms are built with designers in mind, while others focus on helping developers ship code faster. Some specialize in generating polished interfaces, whereas others create complete applications or improve research, branding, and collaboration.

That's what makes today's AI design ecosystem so interesting.

These tools aren't replacing designers or developers; they're changing how they work. Designers can move from an idea to a polished interface faster. Developers spend less time writing repetitive blocks. Product teams validate concepts earlier, iterate more often, and collaborate more efficiently.

The most productive teams in 2026 won't be the ones using the most AI tools. They'll be the ones that know exactly when to use each one.

The takeaway from testing all 24 platforms is:

AI design tools are not experimental productivity boosters. They've become practical parts of modern product development.

And for every step of your work process, there's now an AI tool that can help you move faster without sacrificing quality.

Thanks for reading! 🙏🏻 I hope you found this useful ✅ Please react and follow for more 😍 Made with 💙 by Hadil Ben Abdallah

Hadil Ben AbdallahFollow

Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

What Is Agentic Marketing? How AI Agents Are Replacing the Modern Marketing Stack

Hadil Ben Abdallah — Tue, 28 Jul 2026 08:58:34 +0000

According to Gartner, by 2026, 80% of enterprise marketing organizations are expected to use generative AI in some capacity, highlighting how quickly AI is becoming part of modern marketing operations.

But there's a bigger shift happening behind the scenes. Marketing teams are moving beyond AI assistants that simply generate copy or analyze reports. Instead, they're adopting AI agents that can observe signals, make decisions, execute campaigns, and improve without waiting for human instructions.

This new operating model is called agentic marketing, and it's quickly becoming one of the most important changes in growth engineering.

In this guide, you'll learn exactly what agentic marketing is, how it differs from traditional marketing automation, why it's becoming practical now, and why platforms like Hellyeah AI represent an entirely new category of marketing infrastructure.

What Is Agentic Marketing?

Agentic marketing (also called autonomous marketing automation) is an AI-driven operating model where intelligent marketing agents continuously monitor behavioral data, campaign performance, customer lifecycle events, and business signals, then decide what action to take, execute that action, and learn from the outcome.

Unlike traditional marketing automation, which follows predefined rules created by humans, agentic marketing systems adapt their decisions based on real-time context without requiring someone to initiate every workflow or campaign.

Every agentic marketing system is built around three core capabilities:

Perception: constantly reading real-time signals from marketing channels, products, and customer behavior.
Decision: determining the best action based on current context instead of predefined rules.
Execution + Learning: performing the action, measuring the outcome, and improving future decisions automatically.

This is the fundamental difference between AI assistants and AI agents.

AI assistants help marketers perform tasks.

AI agents operate parts of the marketing function themselves.

Traditional Marketing Automation vs. Agentic Marketing

Dimension	Traditional Marketing Automation	Agentic Marketing
Operating model	Rule-based workflows configured by humans	Decision-based autonomous agents
When it acts	Fixed schedules or predefined triggers	Real-time based on live signals and context
Who initiates actions	Human marketers	AI agents
Adaptability	Static until someone edits workflows	Constantly adapts from new outcomes
Scale	Limited by workflow maintenance	Handles thousands of simultaneous decisions
Learning	Executes rules but doesn't improve itself	Learns from every interaction
Human role	Builds and manages workflows	Sets strategy while agents handle execution
Example	Send an email seven days after signup	Detect behavioral changes instantly and launch the best intervention automatically

The move from the left column to the right isn't just another upgrade to marketing automation.

It's a completely different way of running growth. Instead of asking marketers to configure thousands of workflows, agentic marketing lets AI agents monitor what's happening, determine the best next step, and execute it without waiting for manual intervention.

Why 2026 Is the Inflection Point for Agentic Marketing

Agentic marketing didn't suddenly appear because someone coined a new buzzword. It became practical because several technologies matured at the same time.

The first change was the rapid evolution of large language models. Between 2024 and 2025, AI models became capable of reasoning through multi-step problems, using external tools, remembering context, and making reliable decisions across complex workflows. Those capabilities transformed AI from something that generated content into something that could operate systems.

At the same time, the infrastructure finally caught up. Frameworks for AI agents became significantly easier to deploy, while purpose-built platforms like Hellyeah emerged specifically for marketing rather than adapting generic AI frameworks to marketing use cases. That reduced the engineering effort required to build autonomous marketing operations.

The economics of marketing also changed.

Many growth teams are now expected to manage more acquisition channels and customer touchpoints without significantly increasing headcount. Manual operations simply don't scale at the same rate as modern growth expectations.

Agentic marketing solves that imbalance by increasing execution capacity without forcing marketers to spend their days adjusting bids, launching experiments, reviewing dashboards, or manually moving data between disconnected tools.

The final reason is competitive pressure. Early industry case studies and vendor benchmarks suggest autonomous experimentation can significantly shorten campaign optimization cycles, although results vary depending on implementation maturity.

The conversation is not about whether AI agents will become part of marketing.

It's about how quickly organizations can redesign their marketing operations around them.

The Four Layers of an Agentic Marketing System

Agentic marketing isn't powered by one AI model doing everything. Instead, it operates through several specialized layers that constantly feed information into one another.

Layer	Function	What the Agent Does	Hellyeah Platform
Signal Layer	Reads data across the growth stack	Regularly monitors behavioral events, campaign metrics, customer lifecycle events, competitive signals, and product usage	Mutation, AIMA, Deja Vu, Forge
Decision Layer	Determines the optimal action	Evaluates current context, historical performance, business goals, and customer behavior before selecting the next action	Intelligence shared across all Hellyeah platforms
Execution Layer	Takes action automatically	Launches campaigns, reallocates budgets, sends personalized messages, starts workflows, rotates creatives, and triggers experiments	AIMA, Mutation, Forge, Deja Vu
Learning Layer	Constantly improves future decisions	Measures outcomes, updates optimization strategies, and feeds new insights back into the decision engine	Deja Vu, Mutation, AIMA

Notice how each layer depends on the others.

Without real-time signals, an AI agent makes poor decisions.

Without autonomous execution, even perfect decisions remain ideas.

Without continuous learning, performance eventually plateaus because yesterday's winning strategy becomes tomorrow's outdated one.

This closed feedback loop is what separates agentic marketing from traditional automation.

Hellyeah AI is built around this complete architecture. Rather than offering isolated automation features, it connects signal collection, decision-making, execution, and continuous learning into a shared operating system where each layer reinforces the others over time.

Instead of treating every campaign or workflow as an isolated task, Hellyeah creates a continuous feedback loop.

Every campaign outcome, behavioral response, experiment result, and conversion signal feeds back into the shared data layer. Those insights refine future budget allocation, personalization decisions, experimentation priorities, and workflow execution automatically, allowing every new decision to benefit from what the system has already learned.

Agentic Marketing Platforms Compared

While several AI-powered marketing platforms now automate parts of the marketing workflow, they focus on different layers of the agentic marketing stack. Here's how the major platforms compare.

Platform	Primary Focus	Best For	Pricing	Limitation
Hellyeah AI	Complete agentic marketing platform (signal, decision, execution, learning)	Organizations building an autonomous marketing operation	Enterprise	Requires clean event instrumentation and onboarding before the autonomous workflows deliver full value.
Jasper AI	AI content creation	Marketing teams producing blogs, ads, emails, and social content	Paid	Primarily focuses on content generation and doesn't provide autonomous campaign execution or behavioral decision-making.
HubSpot AI	CRM and marketing automation with AI assistance	Businesses already using the HubSpot ecosystem	Paid / Enterprise	AI capabilities mainly enhance existing HubSpot workflows rather than operating as autonomous marketing agents.
Adobe Sensei	Enterprise creative optimization and personalization	Large enterprises using Adobe Experience Cloud	Enterprise	Best suited for organizations already invested in Adobe's ecosystem and requires significant implementation.
Salesforce Einstein	AI-powered CRM insights and sales/marketing intelligence	Enterprise sales and marketing organizations	Enterprise	Strong customer intelligence but relies heavily on Salesforce infrastructure and customization.
Adobe Marketo Engage	Enterprise marketing automation	Large B2B marketing teams running complex nurture campaigns	Enterprise	Primarily rule-based automation with AI enhancements rather than a fully agentic operating model.

Most of these platforms excel at one layer of the marketing stack, whether that's content generation, CRM intelligence, or marketing automation. Hellyeah approaches the problem differently by combining signal detection, autonomous decision-making, execution, and continuous learning into a single agentic marketing system instead of treating them as separate products.

Hellyeah: The Purpose-Built Platform for Agentic Marketing

Everything we've covered so far leads to this conclusion: agentic marketing requires more than a collection of AI-powered tools. It needs a unified system where AI agents can perceive signals, make decisions, execute actions, and learn from the results over time.

That's exactly what Hellyeah was designed to do.

Unlike general-purpose AI frameworks that can be adapted for marketing, or traditional marketing platforms that have recently added AI features, Hellyeah was built from the ground up around the agentic marketing model. Instead of asking marketers to create workflows, monitor dashboards, and manually launch campaigns, it provides an autonomous execution layer that regularly operates across the entire growth stack.

Hellyeah's four core components work together through a shared data layer. Each one specializes in a different part of the marketing lifecycle while constantly exchanging data with the others, allowing the entire system to become more effective over time.

AIMA: The Autonomous Paid Acquisition Agent

Managing paid acquisition has traditionally required constant manual work. Marketing teams monitor campaign performance, adjust bids, pause underperforming ads, redistribute budgets, rotate creatives, and watch audience performance every day.

AIMA automates that operational layer.

Instead of waiting for someone to log into Google Ads or Meta Ads every morning, AIMA monitors campaign performance in real time. As conversion signals change, it reallocates budgets toward higher-performing audiences, adjusts bidding strategies, rotates creatives before fatigue reduces performance, and keeps campaigns aligned with the growth objectives defined by the team.

Marketing teams still define the overall acquisition strategy and business goals. AIMA handles the continuous optimization required to achieve them.

Mutation: The Behavioral Response Agent

Modern customer journeys rarely follow a predictable path. Some users become highly engaged within hours, while others quietly lose interest long before traditional lifecycle campaigns detect a problem.

Mutation is built for exactly those moments.

It monitors behavioral signals across both the product and marketing stack, looking for meaningful changes instead of waiting for scheduled workflows. A sudden drop in product usage, a customer reaching an activation milestone, repeated visits to a pricing page, or behavior that indicates purchase intent can all trigger immediate responses.

Instead of sending the same generic sequence to every user after seven days, Mutation responds to what each individual customer is doing right now. It can launch personalized re-engagement campaigns, activate lifecycle messaging, trigger product guidance, or initiate custom workflows the moment a meaningful behavioral signal appears.

Deja Vu: The Continuous Experimentation Agent

Most marketing teams run experiments in cycles.

Someone creates a hypothesis, launches an A/B test, waits for statistical significance, analyzes the results, publishes the winner, and eventually starts another experiment weeks later.

Deja Vu turns experimentation into a permanent system instead of a recurring project.

It runs multivariate experiments across landing pages, onboarding experiences, email campaigns, ad creatives, pricing pages, and other customer touchpoints. Instead of waiting for marketers to manually launch the next test, it reallocates traffic toward higher-performing variations automatically while continuing to search for better combinations.

Because every experiment feeds into the next one, optimization compounds over time instead of restarting with each testing cycle.

Marketing teams no longer spend most of their time managing experiments.

They spend their time deciding what business questions are worth exploring while Deja Vu keeps improving execution underneath them.

Forge: The Custom Agentic Workflow Builder

No two companies operate exactly the same way.

Every company has unique sales motions, content strategies, approval processes, outbound sequences, SEO workflows, influencer programs, and internal operations that can't be solved by generic automation templates.

Forge exists to build those custom systems.

Instead of forcing teams into predefined workflows, Forge creates AI agents tailored to each organization's growth strategy. Companies can build autonomous workflows for content production, GEO and SEO operations, outbound prospecting, influencer outreach, user-generated content pipelines, partner activation, lead routing, and countless other marketing processes.

Once deployed, these workflows continue running autonomously while adapting to new signals from the rest of the Hellyeah ecosystem.

As the company's marketing strategy evolves, Forge evolves alongside it.

The Compound Growth Loop

The biggest advantage of Hellyeah isn't any individual product.

It's the fact that all four systems operate from the same shared data layer.

Signals collected by Mutation influence the decisions made by AIMA. Insights discovered through Deja Vu improve campaign optimization across paid acquisition. Forge has access to the same behavioral intelligence that powers the rest of the ecosystem, allowing custom workflows to react using identical real-time context.

Instead of relying on disconnected integrations, every component shares the same behavioral and performance data. Insights generated by one agent immediately become available to the others, allowing every future decision to benefit from previous outcomes.

This creates a compounding feedback loop:

Better signals produce better decisions.
Better decisions produce stronger execution.
Better execution generates richer learning.
Better learning produces even better signals.

Best For

Hellyeah is best suited for growth-stage and enterprise SaaS companies, B2B businesses, e-commerce brands, mobile apps, fintech, gaming, and education companies that want to build an autonomous growth operation instead of managing an ever-growing collection of disconnected marketing tools.

Pricing

Enterprise

Limitation

Hellyeah isn't a plug-and-play product that delivers value within a few hours. Like any serious agentic system, it requires clean event instrumentation, well-defined growth objectives, and proper onboarding before its autonomous workflows can operate at full capacity. Teams that invest in that foundation benefit from compounding improvements over time, but organizations expecting instant results without preparation will likely be disappointed.

Agentic Marketing vs. Traditional Marketing Automation: Real-World Examples

The easiest way to understand agentic marketing is to compare how everyday marketing work changes once AI agents become responsible for execution.

Marketing Task	Traditional Marketing Automation	Agentic Marketing (Hellyeah)
Paid campaign optimization	Marketing managers manually review campaign performance, adjust bids, redistribute budgets, and rotate creatives every few days.	AIMA regularly monitors campaign performance, reallocates budgets, adjusts bids, and rotates creatives in real time without waiting for human intervention.
User re-engagement	Lifecycle emails are scheduled based on predefined rules, such as sending an email seven days after inactivity.	Mutation detects declining engagement as soon as behavioral signals change and launches personalized re-engagement campaigns tailored to each user's activity.
Landing page optimization	Teams plan A/B tests manually, wait for statistical significance, and publish winners before starting another experiment.	Deja Vu constantly runs multivariate experiments, reallocates traffic toward better-performing variants, and keeps optimizing without restarting the testing cycle.
Content and SEO execution	Content calendars are planned quarterly, briefs are created manually, and publishing follows fixed schedules.	Forge can build autonomous content pipelines that identify opportunities, generate briefs, coordinate production, and adapt publishing priorities as search behavior changes.
Outbound prospecting	SDRs manually research prospects, write personalized emails, and manage follow-up sequences themselves.	Forge builds agentic outbound workflows that research accounts, personalize outreach, coordinate follow-ups, and synchronize customer data automatically.

These examples illustrate the core difference between automation and agency.

Automation executes instructions.

Agentic marketing determines what the next instruction should be based on the latest information available.

What Agentic Marketing Is Not

As more companies begin using the term agentic marketing, it's easy to confuse it with existing marketing technologies. Understanding what it isn't is just as important as understanding what it is.

It isn't marketing automation with a new name.

Traditional marketing automation executes workflows that humans configure in advance. If a user performs a specific action, the system follows the rule that someone previously created.

Agentic marketing works differently. AI agents continuously evaluate the current situation, choose the most appropriate action based on context, execute it, and learn from the outcome. The difference isn't better automation; it's autonomous decision-making.

It isn't a replacement for marketing strategy.

AI agents can execute campaigns, optimize budgets, personalize customer journeys, and run experiments, but they don't define your company's positioning, messaging, product vision, or long-term business goals.

Marketers don't disappear; their role evolves. Instead of spending hours adjusting campaigns or maintaining workflows, teams focus on strategy, creativity, brand positioning, and growth priorities while AI handles operational execution.

It isn't plug-and-play software.

Building an autonomous marketing operation requires preparation.

Purpose-built platforms like Hellyeah need clean event instrumentation, connected marketing systems, clear business objectives, and an onboarding phase before autonomous agents can make high-quality decisions.

Companies willing to invest in that foundation benefit from a system that keeps improving over time. Those expecting instant results without proper setup will almost certainly be disappointed.

Frequently Asked Questions

What is agentic marketing?

→ Agentic marketing (also called autonomous marketing automation) is an AI-driven operating model where intelligent agents monitor signals, make decisions, execute marketing actions, and learn from the results. Unlike traditional automation, it adapts constantly based on real-time context instead of following fixed workflows.

How is agentic marketing different from marketing automation?

→ Marketing automation follows rules that humans create in advance, such as "if X happens, do Y." Agentic marketing goes further by evaluating current conditions, deciding the best action, executing it autonomously, and improving future decisions through continuous learning.

What are some real-world examples of agentic marketing?

→ Examples include AI agents that optimize paid campaigns in real time, detect customer churn signals and launch personalized re-engagement campaigns, constantly improve landing pages through experimentation, and automate outbound or SEO workflows without manual intervention.

Which platform is best for agentic marketing?

→ The right platform depends on how mature your marketing operation is. If you're building a fully agentic marketing system instead of adding AI to existing workflows, Hellyeah is purpose-built around that operating model. Its AIMA, Mutation, Deja Vu, and Forge components cover signal detection, decision-making, execution, and continuous learning within a unified system.

Final Thoughts

Agentic marketing represents one of the biggest architectural shifts marketing has seen since the rise of marketing automation.

Instead of managing dozens of disconnected tools and manually coordinating campaigns, organizations are beginning to operate unified AI systems that perceive signals, make decisions, execute actions, and continuously learn from every customer interaction.

Hellyeah embodies that shift. Instead of adding AI features to existing marketing software, it was purpose-built around the four layers that define an agentic marketing system: real-time signal detection through Mutation, autonomous paid acquisition with AIMA, continuous experimentation through Deja Vu, and custom growth workflows powered by Forge.

That doesn't eliminate the need for marketers.

It frees them to focus on what humans do best: strategy, creativity, positioning, and building products people genuinely want, while autonomous agents handle the operational complexity of modern growth.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

BrowserAct vs Agent Browser: A Hands-On Stealth Execution Comparison

Hadil Ben Abdallah — Thu, 16 Jul 2026 07:45:58 +0000

Cloudflare and SannySoft benchmark results

Hadil Ben Abdallah

Jul 13

BrowserAct vs Agent Browser: A Hands-On Stealth Execution Comparison

#ai #agents #webdev #automation

10 min read

AI Customer Success Tools: 7 Platforms That Reduce SaaS Churn and Drive Expansion Revenue in 2026

Hadil Ben Abdallah — Tue, 14 Jul 2026 09:10:36 +0000

Companies with a net revenue retention (NRR) rate above 120% grow three times faster than those below 100%, according to KeyBanc Capital Markets’ SaaS Survey, making customer success one of the highest-leverage growth functions in modern SaaS.

Most SaaS teams still treat customer success as a reactive function: monitor accounts, review health scores, schedule check-ins, and respond when something goes wrong. But the companies scaling efficiently in 2026 are moving toward AI-powered customer success tools that detect behavioral signals, identify expansion opportunities, and trigger the right action before a human review is needed.

This guide compares the 7 best AI customer success platforms (also called CS automation platforms) for SaaS teams that want to improve Net Revenue Retention (NRR), reduce SaaS churn, and create more predictable expansion revenue.

Retention vs. Expansion: Why Customer Success Needs to Own Both

Customer success in 2026 is not only about preventing churn. The highest-performing teams manage two connected outcomes: protecting existing revenue and creating expansion revenue from customers who are already receiving value.

The metric that captures both is Net Revenue Retention (NRR).

NRR measures how much revenue remains from an existing customer base after accounting for expansion, churn, and contraction. A SaaS company with an NRR above 100% can grow even without acquiring new customers because existing accounts are generating additional revenue over time.

The formula is:

NRR = (Starting MRR + Expansion MRR - Churn MRR - Contraction MRR) / Starting MRR × 100

Traditional customer success workflows usually focus on the negative side of the equation: finding unhappy customers before they leave. AI customer success tools expand that view by identifying both risk signals and growth signals.

A declining login frequency, reduced feature usage, or increased support volume may indicate churn risk. But reaching a usage limit, adding teammates, or repeatedly engaging with advanced features may indicate an expansion opportunity.

The timing matters.

The worst moment to introduce an upgrade conversation is during renewal, when customers are already evaluating whether they should continue. The strongest expansion moments happen when users demonstrate value, hitting a feature limit, inviting more teammates, or adopting a workflow that naturally requires a higher plan.

AI-powered CS platforms help identify those moments automatically and connect them to the right intervention.

The Customer Health Score Framework: What AI Customer Success Tools Monitor

A strong customer health score is not just a dashboard metric. It is a combination of behavioral signals that shows whether an account is moving toward retention, expansion, or risk.

The best CS teams combine product usage data, customer feedback, support interactions, and revenue signals to create a complete picture of account health.

Health Signal	Data Source	Weight in Health Score	CS Action When Score Drops
Product engagement depth	Product analytics tools like Mixpanel and Amplitude	High (25–30%)	Trigger feature adoption guidance or targeted CSM outreach
Login frequency and session length	Product event stream	High (20–25%)	Launch re-engagement workflow or flag account risk
Support ticket volume and sentiment	Support platforms like Intercom	Medium (15–20%)	Escalate support issues and prioritize outreach
NPS / CSAT score	Customer feedback surveys	Medium (15%)	Contact detractors quickly and identify promoters
Seat utilization	CRM + product data	High (20–25%)	Detect contraction risk or expansion opportunities
Renewal proximity	CRM and billing data	Situational	Start renewal workflows and executive engagement
Expansion signals	Product events, feature usage, limits reached	Situational	Trigger expansion messaging at peak intent

The important difference between traditional CS reporting and AI-driven customer success is response speed.

A weekly health score review might show that an account has become unhealthy. A real-time behavioral system can detect multiple declining signals while they are happening and route the right action immediately.

AI Tools for SaaS Customer Success (2026 Comparison)

Tool	Category	Best For	Pricing Tier
Gainsight	Enterprise CS platform + health scoring + renewal management	Large SaaS companies with complex customer success operations	Enterprise
Hellyeah	Real-time post-activation behavioral tracking + expansion automation	SaaS teams wanting at-risk detection and expansion nudges to run autonomously	Enterprise
ChurnZero	Customer success + health scoring + expansion playbooks	Mid-market SaaS teams managing structured account portfolios	Paid / Enterprise
Totango	Modular CS platform + customer journey automation	Teams wanting flexible CS workflows without heavy implementation	Paid / Enterprise
Planhat	CS operations + revenue management	CS and RevOps teams aligning customer activity with revenue outcomes	Paid / Enterprise
Vitally	B2B SaaS CS platform + health scoring	Mid-market SaaS teams wanting faster deployment and usability	Paid
Intercom	Conversational CS + AI-assisted expansion messaging	SaaS teams using chat-led support and low-touch customer engagement	Paid (Free limited)

These customer success tools help SaaS teams move beyond reactive account management by combining behavioral signals, health scores, and AI-driven workflows.

Gainsight — Enterprise Customer Success Platform for Complex SaaS Operations

Gainsight is designed for SaaS companies where customer success has become a large operational function with dedicated teams, complex account structures, and multiple renewal workflows.

The platform acts as a central system of record by combining product usage data, CRM information, support interactions, and customer feedback into customer health scores. This gives CS leaders visibility across thousands of accounts and helps teams prioritize where human attention is required.

Its strength is operational depth. Large organizations can build renewal playbooks, QBR processes, escalation workflows, and executive engagement motions that standardize customer success across regions and teams.

Gainsight also includes AI capabilities through its Horizon AI layer, helping teams identify risks, recommend next actions, and automate certain customer success activities.

However, the complexity that makes Gainsight powerful also makes implementation demanding. Teams need dedicated CS operations resources to configure workflows, maintain integrations, and ensure adoption across customer-facing teams.

Best for: Enterprise SaaS companies with large CS organizations, complex renewal cycles, and multi-product account structures.

Limitation: Implementation requires significant time, operational resources, and investment. Smaller SaaS teams may not have enough complexity to justify the deployment effort.

Hellyeah — AI-Native Customer Success Automation for Retention and Expansion

Hellyeah AI is an AI-native growth engine that connects post-activation behavioral signals directly to autonomous retention and expansion actions.

Most CS platforms are designed around the workflow:

Collect data → calculate health score → notify the team → manually decide the next step

Hellyeah changes that loop into:

Detect signal → act immediately → learn from results → improve continuously

The core of this approach is Hellyeah’s Mutation layer, which monitors post-activation customer behavior and identifies changes that indicate either risk or expansion opportunity.

For example, if an account’s usage drops across multiple dimensions, fewer logins, lower feature adoption, and reduced team activity, Mutation can flag the account before a CSM notices it during a weekly review.

But the same mechanism works in the opposite direction.

When a customer reaches a feature limit, adds new teammates, or shows repeated usage of advanced functionality, Mutation can identify the expansion signal and trigger the right next step: an in-app upgrade prompt, personalized message, or CSM notification.

The difference is timing.

An expansion conversation sent during renewal is often too late because the customer has already formed an opinion about the product’s value. A message triggered when users actively experience value appears at the moment intent is highest.

Hellyeah’s other layers extend this beyond detection.

Mutation handles real-time behavioral detection and response.
Deja Vu continuously experiments with expansion and retention interventions. Instead of manually testing one upsell message every few months, Deja Vu evaluates which message, timing, and segment combination performs best and reallocates toward stronger variations.
Forge enables custom AI agentic workflows around unique CS operations, including health score calculations, escalation routing, QBR preparation, and account-specific processes.
AIMA extends the lifecycle beyond the product by enabling targeted campaigns for accounts that need additional reinforcement across channels.

Together, these components create a customer success operation that compounds over time. Fewer at-risk accounts, more expansion opportunities, and less manual analysis for customer success teams.

The result is not replacing CSMs. It is making every CSM interaction higher leverage by ensuring teams spend time on the accounts where human judgment matters most.

Best for: SaaS companies that want post-activation health monitoring, churn prevention, and expansion automation to run continuously without relying on manual account reviews.

Limitation: Hellyeah requires clean product event instrumentation and reliable customer data connections before it can deliver full value. Teams without a strong event taxonomy or structured CRM data will need to improve their data foundation first.

ChurnZero — Customer Success Platform for Mid-Market SaaS Teams

ChurnZero focuses on helping mid-market SaaS companies manage customer relationships through health scoring, automated playbooks, and account-level visibility.

The platform combines product usage, CRM data, and customer interactions to identify accounts that require attention. CS teams can create automated workflows for onboarding, adoption milestones, renewal preparation, and expansion opportunities.

Where ChurnZero performs well is structured customer success operations. Teams with dedicated CSMs can use it to manage portfolios, monitor account health, and create repeatable processes instead of relying on spreadsheets and manual tracking.

Its automation capabilities are particularly useful for companies managing hundreds of customer accounts where personalized attention is difficult to maintain manually.

However, ChurnZero is built around a CSM-led customer success model. Companies that rely primarily on product-led growth and self-service expansion may not benefit from all of its capabilities.

Best for: Mid-market SaaS companies with customer success teams managing structured account portfolios.

Limitation: Less effective for PLG companies without dedicated CSM workflows because its strongest features depend on human-led customer success motions.

Totango — Modular Customer Success Platform for Flexible CS Operations

Totango is designed for SaaS teams that need a customer success platform without adopting the complexity of a fully enterprise-focused system. Its modular approach allows teams to build customer journeys around specific lifecycle stages such as onboarding, adoption, renewal, and expansion.

The platform uses configurable SuccessBLOCs, which are pre-built frameworks for common customer success workflows. Teams can activate the modules they need, define health metrics, create playbooks, and automate customer interactions without rebuilding their entire CS operation from scratch.

This flexibility makes Totango attractive for growing SaaS companies that have moved beyond spreadsheets but are not ready for the operational overhead of large enterprise CS platforms.

Its customer journey capabilities are especially useful for teams managing different customer segments with different success criteria. A small business customer and an enterprise account can follow completely different engagement paths while still being managed from the same platform.

However, flexibility also creates a tradeoff. Teams often need to invest time defining their own processes, metrics, and workflows before they can extract maximum value.

Best for: SaaS companies that want a configurable customer success platform with modular workflows and faster adoption than traditional enterprise solutions.

Limitation: Large enterprises with highly complex account structures may eventually need deeper customization and broader integrations than Totango provides.

Planhat — Customer Success Operations Platform for Revenue Alignment

Planhat focuses on connecting customer success activities with measurable revenue outcomes. Instead of treating CS as a support function, it gives teams visibility into metrics that directly impact growth, including retention, expansion revenue, contraction, and customer health.

The platform combines customer data from CRM systems, product analytics, and billing platforms into customizable dashboards. This allows CS and RevOps teams to work from the same data foundation when forecasting renewals or identifying expansion opportunities.

One of Planhat’s strongest advantages is flexibility. Teams can customize workspaces, dashboards, and workflows around their specific operating model instead of adapting everything to a rigid structure.

For SaaS companies where customer success owns expansion revenue, this alignment is valuable because it creates clearer accountability between customer outcomes and revenue performance.

The tradeoff is that flexibility requires operational maturity. Teams without clear processes may spend significant time designing their own workflows instead of immediately benefiting from predefined best practices.

Best for: B2B SaaS companies where customer success and revenue operations need a shared system for retention and expansion planning.

Limitation: A smaller ecosystem of native integrations compared with larger enterprise platforms can require additional API work for complex data environments.

Vitally — Fast-to-Deploy Customer Success Platform for B2B SaaS Teams

Vitally is built for SaaS teams that need structured customer success operations without the long implementation cycles often associated with enterprise platforms.

It provides customer health scoring, account management workflows, task automation, and playbook functionality through a user experience designed around daily CSM workflows.

Its main advantage is speed. Teams can connect common SaaS data sources, configure customer health models, and start managing accounts without months of operational setup.

Vitally is particularly popular among B2B SaaS companies that have reached the stage where customer relationships require more structure but still want a platform that feels lightweight and easy for customer-facing teams to adopt.

The platform also supports automated workflows that help CSMs manage onboarding, renewal preparation, and customer engagement activities more consistently.

However, its simplicity comes with limitations. Companies with thousands of accounts, multiple product lines, and highly complex enterprise renewal processes may eventually need a more comprehensive enterprise CS system.

Best for: Growing B2B SaaS companies that need a modern customer success platform with faster deployment and strong usability.

Limitation: Less suitable for large enterprises requiring highly complex account hierarchies, advanced governance, and extensive renewal operations.

Intercom — Conversational Customer Success and AI-Assisted Engagement

Intercom approaches customer success from the conversation layer. Instead of acting primarily as a customer health database, it focuses on helping SaaS teams communicate with users through AI-powered support, messaging, and in-product interactions.

Its AI agent, Fin, helps resolve customer questions automatically, reducing support friction that can contribute to churn. Product tours and targeted messages also allow teams to guide users toward important features and adoption milestones.

For product-led SaaS companies, this conversational approach can be powerful because many customer interactions happen directly inside the product rather than through scheduled CSM calls.

Intercom can also support expansion conversations by identifying opportunities for targeted messaging based on user behavior and engagement patterns.

However, it is not a dedicated customer success operating system. Teams looking for portfolio-level health scoring, renewal forecasting, and expansion pipeline management will typically need additional CS infrastructure.

Best for: SaaS companies with chat-driven customer engagement models and teams that want AI-assisted support and in-product communication.

Limitation: It lacks deep customer success management capabilities such as account health scoring, renewal management, and revenue forecasting.

The NRR Optimization Playbook: What High-Performing CS Teams Do Differently

The best customer success teams in 2026 do not measure success only by how many accounts they save. They build systems that protect existing revenue while continuously creating expansion opportunities.

Net Revenue Retention (NRR) has become the central metric because it measures the complete customer lifecycle: what revenue stays, what revenue expands, and what revenue disappears.

A SaaS company with NRR above 100% can grow its revenue base even without acquiring new customers because existing customers are increasing their spending over time.

A modern CS operation should focus on five principles:

1. Define NRR as the customer success north star

Churn rate only tells you what was lost. NRR shows the complete picture by combining retention and expansion.

Customer success teams that optimize only for churn reduction often miss opportunities to grow existing accounts. Expansion revenue from additional seats, upgraded plans, and new product adoption should be treated as a core CS responsibility.

2. Build health scores that predict expansion, not only risk

Most health scores answer one question:

“Which customers might leave?”

Advanced CS teams ask a second question:

“Which customers are ready to grow?”

Signals such as increased feature usage, new team members, and approaching usage limits can indicate expansion opportunities before a customer explicitly asks for an upgrade.

3. Automate the long tail and focus humans where they matter

CS teams cannot manually review every account every day.

AI customer success tools can monitor behavioral signals, update health scores, and trigger workflows automatically. This allows CSMs to spend more time on strategic conversations with high-value accounts.

The goal is not replacing customer success managers. It is increasing the leverage of every interaction.

4. Continuously test expansion messaging

The best expansion strategy today may not be the best strategy six months from now.

Customer behavior changes, markets shift, and different segments respond differently. AI experimentation layers like Hellyeah's Deja Vu help teams continuously test which message, timing, and channel creates the strongest expansion response.

5. Connect CS operations with revenue teams

Expansion revenue should not exist as an informal opportunity hidden inside customer conversations.

High-performing SaaS companies connect customer success data with revenue operations so expansion opportunities become visible pipeline instead of unexpected wins.

Frequently Asked Questions

What is the best AI tool for SaaS customer success in 2026?

→ The best AI customer success tool depends on company size, customer model, and CS maturity. Enterprise teams often use Gainsight, while mid-market SaaS companies may prefer ChurnZero or Vitally.
For autonomous post-activation monitoring and expansion optimization, Hellyeah AI combines behavioral detection, workflow automation, and experimentation through Mutation and Deja Vu.

What is Net Revenue Retention (NRR) and why does it matter for SaaS companies?

→ Net Revenue Retention (NRR) measures how much revenue a SaaS company keeps and expands from existing customers over time.
It includes expansion revenue, upgrades, churn, and contraction, making it a stronger growth metric than churn rate alone. An NRR above 100% means the existing customer base is growing without new acquisition.

What is the difference between customer retention and customer success?

→ Customer retention focuses on preventing churn by identifying risks and keeping existing customers active.
Customer success takes a broader approach by improving adoption, helping customers achieve value, and creating expansion opportunities.
Retention prevents loss, while customer success drives long-term growth and revenue expansion.

How do AI tools improve customer success team efficiency?

→ AI customer success tools automate manual account reviews by continuously analyzing product usage, support activity, and CRM data.
They detect behavioral signals earlier and help teams prioritize the right actions.
Tools like Hellyeah's Mutation enable real-time responses, while Deja Vu improves engagement through continuous experimentation.

Final Thought

Customer success is no longer about creating more dashboards and hoping teams discover problems faster.

The highest-performing SaaS companies build systems that detect behavioral changes automatically, identify expansion opportunities at the right moment, and route every signal to the right action.

The future of customer success is not more manual account reviews. It is intelligent infrastructure that helps every CSM focus on the conversations where human judgment creates the most value.

Thanks for reading! 🙏🏻 Please follow Hadil Ben Abdallah & Hellyeah for more 🧡

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Software Engineer • Technical Writer (300K+ readers & 20K+ followers) • Trusted by 10+ companies I turn brands into websites people 💙 to use

DEV Community: Hadil Ben Abdallah

Mozaik Hackathon 2026: Build Concurrent Multi-Agent Systems and Compete for $1,000 in Cash Prizes

TL;DR

Table of Contents

Sequential vs. Concurrent Multi-Agent Systems: What Changes With Mozaik?

How Mozaik’s Event-Driven Architecture Enables Concurrent AI Agents

AgenticEnvironment: A Shared Event-Driven Layer

Non-Blocking Inference Keeps Agents Moving

Participants Keep Agents Loosely Coupled

Getting Started With Mozaik: A TypeScript Runtime for Concurrent Agents

Mozaik Hackathon 2026: Build a Concurrent Multi-Agent System

Who can participate in the Hackathon?

Mozaik Hackathon Timeline: September 4–13, 2026

What Can You Build With Mozaik?

Why Developers Should Join the Mozaik Hackathon

You can learn by building alongside other developers

There is a real incentive, too

Frequently Asked Questions

What makes Mozaik different from other AI agent frameworks?

Do I need experience with Mozaik to join the hackathon?

What will I build during Mozaik hackathon?

When does the Mozaik hackathon take place, and is it free?

What prizes can Mozaik hackathon participants win?

Final Thoughts

Hadil Ben AbdallahFollow

Best Enterprise MCP Gateway for Security & Governance in 2026: A Practical Guide to Securing AI Agent Tool Access

TL;DR

What Is an Enterprise MCP Gateway?

MCP Gateway vs. Direct MCP Connections

Enterprise MCP Gateway vs. MCP Proxy

Why MCP Security Has Become an Enterprise Problem

Why MCP Security Requires More Than Authentication

What Should an Enterprise MCP Gateway Provide?

MCP Authentication and Authorization

MCP Tool Filtering and Access Control

Audit Logs and MCP Observability

Guardrails and Policy Enforcement

AI Governance and Cost Control

How Bifrost Approaches Enterprise MCP Governance

How Bifrost Edge Extends MCP Governance to Developer Endpoints

What Enterprise MCP Governance Looks Like in Practice

What to Look for in the Best Enterprise MCP Gateway

How to Choose the Best Enterprise MCP Gateway in 2026

A Practical Enterprise MCP Governance Checklist

Frequently Asked Questions

What is an enterprise MCP gateway?

Why do enterprises need an MCP gateway?

How does an MCP gateway secure AI agents?

What is the difference between an MCP gateway and an MCP proxy?

Can an enterprise MCP gateway control individual MCP tools?

Final Thoughts

Hadil Ben AbdallahFollow

I Tested 5 Design to Code Tools With the Same Outdated SaaS Dashboard

TL;DR

What Are Design to Code Tools?

Testing 5 Design to Code Tools With the Same SaaS Dashboard

The Outdated SaaS Analytics Dashboard

The Prompt I Gave Every Tool

How I Compared the Results: Evaluation Criteria

Quick Comparison Table

1. Flowstep

What It Generated

Key Features

Pricing

2. Anima

What It Generated

Key Features

Pricing

3. v0 by Vercel

What It Generated

Key Features

Pricing

4. Lovable

What It Generated

Key Features

Pricing

5. Bolt.new

What It Generated

Key Features

Pricing

`AgenticEnvironment`: A Shared Event-Driven Layer