DEV Community: Gayatri Sachdeva

LangGraph vs Langchain + top 10 other alternatives

Gayatri Sachdeva — Thu, 19 Mar 2026 08:01:36 +0000

LangGraph alternatives are getting more attention because the agent tooling market is splitting into clearer layers.

LangGraph has become one of the best known frameworks for building stateful, graph based agent workflows. The official docs position it around durable execution, human in the loop support, streaming, and stateful orchestration. LangGraph also explicitly recommends higher level LangChain agents for people who want more abstraction, which tells you where it sits in the stack.

That matters because teams searching for LangGraph alternatives are often not looking for exactly the same thing.

Some want another developer framework for multi agent orchestration. Some want a simpler visual builder. Some want an enterprise ready platform around agent execution, governance, and observability. And some just want to stop wiring too much infrastructure around the framework themselves.

This guide compares the top LangGraph alternatives across those different layers so you can tell the difference between a true framework alternative and a broader platform alternative.

What is an AI agent orchestration framework?

An AI agent orchestration framework is a development layer used to define how agents reason, use tools, manage state, coordinate with other agents, and move through multi step tasks. In this category, the real differences usually come down to abstraction level, state handling, developer control, and how much production infrastructure the framework leaves to you.

How we selected the tools for this comparison

We selected these tools based on three different but related roles they play in the market.

First, some are direct LangGraph alternatives because they help developers build and orchestrate stateful agent workflows. Second, some are visual or lower code alternatives that become relevant when teams want to build agents without hand coding every graph. Third, some are broader platforms that matter because many LangGraph users eventually need governance, integrations, observability, and runtime execution around the agent itself.

That is why this list includes direct frameworks like CrewAI, AutoGen, OpenAI Agents SDK, LlamaIndex, Microsoft Agent Framework, and PydanticAI, plus adjacent but relevant platforms like DronaHQ, Langflow, Flowise, and ZenML.

What is LangGraph?

LangGraph is a framework for building long running, stateful AI agent workflows with explicit control over execution, memory, and branching. It is strongest when teams want graph based orchestration and developer level control over how agents move through a task, but it leaves much of the surrounding production layer to the team.

Key features

- Stateful graph based agent orchestration
- Durable execution and checkpoints
- Human in the loop controls
- Streaming and debugging support
- Works with LangChain components but can be used independently

Pricing

- LangGraph is part of the open source LangChain ecosystem
- Infrastructure and deployment costs depend on how you run it

Limitations

- Lower level than many teams actually need
- Production runtime concerns often remain your responsibility
- Steeper learning curve than visual or higher level alternatives

Best suited for

LangGraph is best suited for developers who want explicit control over stateful agent behavior and multi step orchestration. It makes the most sense when graph structure, checkpoints, and custom execution logic matter more than speed of setup or business friendly tooling.

Best LangGraph alternatives

- DronaHQ for teams that need the execution and governance layer around AI agents.
- CrewAI for teams that want a higher level multi agent orchestration framework.
- AutoGen for developers focused on multi agent collaboration patterns.
- OpenAI Agents SDK for teams that want a lighter framework with tracing and agent primitives.
- LlamaIndex for agent workflows that revolve around retrieval, data, and event driven orchestration.
- Microsoft Agent Framework for teams that want Microsoft backed enterprise grade agent infrastructure.
- PydanticAI for typed, structured Python agent workflows.
- Langflow for teams that want a visual builder on top of agent and LLM workflows.
- Flowise for open source visual AI agent flows with self hosting flexibility.
- ZenML for teams that need the production and MLOps layer around agent workflows.

DronaHQ

Overview

DronaHQ is not a direct framework swap for LangGraph. It is the platform layer many teams end up needing around agent systems once they move into real business workflows. It is built for agents that need governed access to APIs, databases, and operational systems, plus execution, tracing, and controls in production.

Key features

- AI agents connected to APIs and enterprise systems
- Multi agent orchestration with tools and triggers
- Built in RAG traces and observability
- JavaScript and Python execution for custom logic
- Guardrails masking and human review controls

Pricing

- Starter starts at $100 a month billed annually on the pricing page
- Higher plans and enterprise options scale by usage and deployment needs

Limitations

- Not a Python graph framework in the LangGraph mold
- Better for operational execution than framework level experimentation
- Requires clear workflow design to get the most value

Best suited for

DronaHQ is best suited for teams that have moved beyond pure agent orchestration and now need an execution environment around agents. It fits when the job is not only to design agent logic, but to run agents safely across business systems with governance and visibility.

CrewAI

[caption id="attachment_37418" align="alignnone" width="2560"] Screenshot[/caption]

Overview

CrewAI is one of the clearest LangGraph alternatives for teams that want a higher level multi agent framework. It is designed around crews of agents working together on tasks, with a more opinionated abstraction than LangGraph and a stronger focus on role based collaboration.

Key features

- Multi agent orchestration around crews and tasks
- Higher level abstractions than graph level orchestration
- Open source framework plus commercial platform
- Enterprise messaging around control and scale
- Visual and hosted options alongside the framework

Pricing

- Open source framework is available
- Professional plan is listed at $25 a month and enterprise is custom on CrewAI pricing

Limitations

- More opinionated than LangGraph in how agents are structured
- Less explicit graph level control than LangGraph users may want
- Production use may still require broader platform choices

Best suited for

CrewAI is best suited for teams that want multi agent orchestration without working at the graph primitive level. It is a strong fit when the team prefers role based agent collaboration and faster setup over lower level control.

AutoGen

Overview

AutoGen is a Microsoft backed framework for building AI agents and multi agent applications. It has been one of the most visible LangGraph alternatives for developers interested in agent to agent collaboration, dynamic workflows, and more research friendly agent patterns.

Key features

- Multi agent application framework
- Support for deterministic and dynamic workflows
- Strong history in research and experimentation
- Open source and developer oriented
- Human and autonomous collaboration patterns

Pricing

- AutoGen is open source
- Deployment and model costs depend on your stack

Limitations

- Microsoft now points new users toward Microsoft Agent Framework
- Can feel more experimental than production focused in some teams
- Surrounding operational concerns remain external

Best suited for

AutoGen is best suited for developers who want to experiment with multi agent collaboration patterns and build custom agent systems in code. It is strongest when the focus is agent interaction design rather than enterprise operationalization.

OpenAI Agents SDK

Overview

OpenAI Agents SDK is a lightweight framework for building agentic applications with tools, handoffs, and tracing. It is a strong LangGraph alternative for teams that want fewer abstractions and faster entry into production agent workflows without committing to graph first orchestration.

Key features

- Lightweight agent primitives and tool calling
- Handoffs between specialized agents
- Built in tracing support
- Python and TypeScript support
- Production friendly without heavy abstraction

Pricing

- Framework usage is tied to model and API costs
- No separate framework license is required

Limitations

- Less explicit graph structure than LangGraph
- Best fit is narrower if you want full custom orchestration graphs
- Deep platform concerns still need to be handled elsewhere

Best suited for

OpenAI Agents SDK is best suited for teams that want a lighter agent framework with modern primitives and tracing out of the box. It works well when speed and simplicity matter more than detailed graph control.

LlamaIndex

Overview

LlamaIndex is often considered a LangGraph alternative when agent workflows revolve around retrieval, documents, and data intensive context handling. Its workflows layer supports multi step and multi agent patterns, but its center of gravity is still data aware AI systems rather than graph orchestration alone.

Key features

- Agent workflows built on event driven workflow foundations
- Strong retrieval and data orchestration ecosystem
- Good fit for RAG and document heavy agents
- Multi agent workflow support
- Strong developer documentation around workflow patterns

Pricing

- Open source framework is available
- Cloud and enterprise products vary by deployment path

Limitations

- Not as graph explicit as LangGraph for some orchestration needs
- Broader product surface can feel more complex to evaluate
- Best fit is strongest when retrieval is central to the workflow

Best suited for

LlamaIndex is best suited for teams whose agent workflows depend heavily on retrieval, documents, and structured access to context. It is a better fit than LangGraph when data handling is the core challenge rather than graph semantics.

Microsoft Agent Framework

Overview

Microsoft Agent Framework is Microsoft’s newer open source runtime and SDK for agentic AI applications. Microsoft describes it as the direct successor that combines AutoGen style multi agent patterns with Semantic Kernel style enterprise features such as state management, type safety, telemetry, and filters.

Key features

- Open source SDK and runtime for agentic AI
- Enterprise oriented state management and telemetry
- Type safety filters and session handling
- Successor positioned above AutoGen and Semantic Kernel concepts
- Strong Microsoft ecosystem alignment

Pricing

- Framework is open source
- Infrastructure and model costs depend on deployment choices

Limitations

- Newer category positioning means some teams may find it evolving fast
- Best fit is stronger for Microsoft aligned stacks
- Broader ecosystem maturity is still catching up in places

Best suited for

Microsoft Agent Framework is best suited for teams that want enterprise grade agent infrastructure with strong Microsoft backing. It is particularly relevant if LangGraph feels too low level and AutoGen feels too research oriented.

PydanticAI

Overview

PydanticAI is a Python agent framework built around typed outputs, validation, and structured developer ergonomics. It is a credible LangGraph alternative for teams that care less about graph metaphors and more about predictable, strongly typed agent workflows inside a Python application stack.

Key features

- Typed agent development in Python
- Strong validation and structured outputs
- Clean developer ergonomics for Python teams
- Focus on reliability and explicitness
- Good fit for controlled agent workflows

Pricing

- Framework is open source
- Costs depend on hosting and model providers used

Limitations

- Less oriented toward graph based orchestration than LangGraph
- Smaller ecosystem than older agent frameworks
- Better for Python heavy teams than mixed stack environments

Best suited for

PydanticAI is best suited for Python teams that want more type safety and predictability in agent development. It makes sense when structured outputs and validation matter more than explicit graph orchestration.

Langflow

Overview

Langflow is a visual builder for LLM and agent workflows. It is relevant in this comparison because many teams searching for LangGraph alternatives are actually searching for a less code heavy way to build similar systems. Langflow trades lower level control for faster visual composition.

Key features

- Visual builder for LLM and agent workflows
- Better fit for teams avoiding graph code by hand
- Open source and developer extensible
- Strong relevance for RAG and tool using agents
- Useful bridge from experimentation to structured AI systems

Pricing

- Open source version is available
- Commercial deployment options depend on hosting path

Limitations

- Not as precise as LangGraph for graph level control
- Less suited for teams that want code first orchestration
- Operational integrations are not its primary strength

Best suited for

Langflow is best suited for teams that want to build agent and retrieval workflows visually instead of encoding everything as framework logic. It is more relevant when speed of iteration matters more than granular orchestration control.

Flowise

Flowise is another open source visual alternative in this space. It is often compared with Langflow more than with LangGraph directly, but it still becomes relevant when the buyer wants a visual route into agent flows, RAG systems, and open source AI building rather than graph programming.

Key features

- Visual builder for AI agents and LLM flows
- Open source with self hosted flexibility
- Strong fit for RAG and agent style systems
- Easier entry into AI orchestration than code only stacks
- Community driven ecosystem around AI workflows

Pricing

- Open source version is available
- Hosted and commercial options depend on deployment path

Limitations

- Not a graph first developer framework like LangGraph
- Requires more AI workflow context than general no code tools
- Enterprise governance is lighter than larger commercial platforms

Best suited for

Flowise is best suited for teams that want an open source visual route into AI agents. It is a better fit when the goal is building and testing agent flows quickly, not managing graph based orchestration at a low level.

ZenML

Overview

ZenML is not a direct LangGraph replacement in the framework sense. It becomes relevant because many teams using LangGraph eventually need the surrounding production layer around agent workflows, including deployment, monitoring, reproducibility, and MLOps style controls.

Key features

- Production and MLOps layer around AI workflows
- Monitoring and reproducibility oriented positioning
- Strong fit for operationalizing complex AI systems
- Useful for teams moving from framework to production discipline
- Broader lifecycle support than framework only tools

Pricing

- Platform pricing depends on deployment and commercial model
- Open source and commercial elements vary by product path

Limitations

- Not a direct graph orchestration framework
- Better as a surrounding layer than a framework swap
- Less useful if you only need agent logic primitives

Best suited for

ZenML is best suited for teams that already know how to build agent workflows but now need stronger production discipline around them. It matters more after the framework choice than at the first orchestration decision.

Benefits of using an AI agent orchestration framework

The biggest benefit is that these frameworks make it possible to manage agent behavior beyond a single prompt and response.

They let teams define how agents use tools, carry state, recover from failures, and coordinate across multiple steps or multiple agents. That becomes important as soon as the problem involves memory, branching, handoffs, retries, or intermediate reasoning rather than just one clean API call.

A good framework also gives developers more control over what the system is actually doing. That matters because agent workflows become much harder to debug once the logic is hidden inside ad hoc scripts or fragile prompt chains.

Which AI agent orchestration tool should you choose

Choose CrewAI or AutoGen if you want a more direct LangGraph alternative for multi agent orchestration in code.

Choose OpenAI Agents SDK if you want a lighter framework with modern agent primitives and tracing.

Choose LlamaIndex if retrieval and data rich context are central to the workflow.

Choose Microsoft Agent Framework if you want enterprise grade agent infrastructure with Microsoft backing.

Choose PydanticAI if your priority is typed, structured Python agent development.

Choose Langflow or Flowise if you want a visual builder instead of graph code.

Choose DronaHQ if you need the platform layer around AI agents, especially when those agents must run across APIs, databases, and real business systems with governance and observability.

Getting started with DronaHQ

If your team is moving from framework experimentation into real business workflows, start with one use case where the agent has to retrieve context, use tools, and take actions across operational systems. That is usually where the limits of framework only thinking start showing up.

DronaHQ Agentic Platform is one place to test that shift in a real workflow. You can also compare it with other low code AI agent builders if you want a broader view of how the execution layer around agents is evolving.

What is an agentic AI platform? How it differs from workflow automation

Gayatri Sachdeva — Wed, 18 Mar 2026 11:27:37 +0000

A lot of teams say they want an AI agent. What they often build first is a workflow.

It looks close enough in a demo. A ticket comes in, a model classifies it, a few rules run, an action gets triggered, and everyone in the room starts calling it agentic. The confusion usually shows up later, when the system has to interpret a fuzzy request, decide which tool to use, pull context from the right source, recover from a bad intermediate result, or ask for help instead of guessing.

That is the point where language stops being cosmetic and starts becoming architectural.

The category is moving fast. Gartner predicted in August 2025 that up to 40% of enterprise applications will include integrated task-specific AI agents by 2026, up from less than 5% in 2025. At the same time, it also warned that more than 40% of agentic AI projects could be cancelled by the end of 2027 because of cost, unclear value, or weak risk controls. That combination tells you something important: Interest is real, but the confusion is also real.

This guide explains what an agentic AI platform actually is, how it differs from workflow automation, and why that distinction matters if you want to build systems that survive beyond the demo and make it into production.

Why is this category so confusing right now?

The market is not struggling because agentic AI is too advanced to understand. It is struggling because too many different product types are being described with the same label.

Right now, “agentic AI platform” can refer to at least four very different things.

It might mean a workflow automation tool with an LLM step added to the middle.
It might mean a builder for tool-using agents.
It might mean an orchestration layer that handles planning, context retrieval, and execution.
Or it might mean a governance and deployment environment wrapped around agents built elsewhere.

A workflow product can include AI reasoning. An agent platform can still rely on deterministic workflows under the hood. A model framework can help create agent behaviour without being a platform in the full sense. Once all of those products start using the same language, the category gets blurry fast.

There is also a marketing reason. “Automation” sounds mature and operational. “Agentic” sounds like the future. So a lot of products are now positioned somewhere between the two. Gartner even has a term for this broader hype problem. In its June 2025 forecast about failed agentic projects, it warned of “agent washing,” where products are marketed as agentic without actually delivering the capabilities teams assume the label implies (source).

That matters because teams end up buying for the word instead of buying for the job to be done.

If what you need is deterministic task automation with a bit of AI support, a workflow tool may be perfect. If what you need is a system that can interpret a goal, choose tools at runtime, retrieve context from business systems, and adapt its path as conditions change, you are in a different category entirely.

What workflow automation actually does well

Workflow automation is not a lesser category in this conversation. It is the more mature one.

A workflow system is designed to follow a known path. A trigger happens, a condition is checked, a rule routes the work, and the next step is determined ahead of time. That deterministic design is exactly why workflow tools are so useful. When the process is repetitive, structured, and predictable, you usually do not want open ended reasoning. You want consistency.

Think about a few common examples:

If an invoice arrives, extract the fields, validate them, and send the approval request to the right person.
If a support ticket contains a specific keyword and comes from a certain account tier, route it to the correct queue.
If a lead submits a form, enrich the record, update the CRM, and trigger the correct follow up sequence.

These are excellent workflow problems because the path is mostly known in advance. The value comes from giving the system freedom to decide what the job is.

This is why workflow automation often scales so well inside operations teams. It reduces variance. It makes behaviour inspectable. It is easier to test because the branches are explicit. It is easier to govern because the acceptable paths are already defined.

The trouble starts when variance is the job.

Once the system has to resolve ambiguity, pick between tools dynamically, inspect intermediate results, revise its plan, or decide whether it has enough context to proceed, predefined branching starts to feel strained. You can still keep adding more rules, more fallback paths, and more exception handling. Many teams do. But at some point you are no longer simplifying the problem. You are just hardcoding around uncertainty.

That is usually the moment people start describing their workflow as an agent, even though the system is still fundamentally rule driven.

What makes a system agentic

A system starts to feel agentic when the next step is not fully hardcoded in advance.

That does not mean it becomes magical, autonomous, or somehow free from structure. It means the system is working from a goal rather than a locked sequence of steps. It can interpret what needs to happen, decide how to proceed, use tools as needed, and adapt when the first path does not work.

This is the practical difference. A workflow says, “when X happens, do Y.” An agentic system says, “given this objective, figure out the best next action within these constraints.”

That usually includes a few recognisable behaviours. The system can reason through ambiguity instead of only matching a rule. It can choose between multiple tools instead of following a single predetermined path. It can retrieve context from the right source when the answer is not already in memory. It can inspect an intermediate result and decide whether to continue, revise, escalate, or ask for more information.

A simple example makes this easier to see. Imagine a procurement request comes in for a software purchase. A workflow can route the request based on threshold, category, and department. An agentic system can go further. It can read the request, identify what is missing, check vendor history, review budget status, compare the request against policy, ask clarifying questions if the request is incomplete, and then decide whether to approve, reject, or escalate.

The difference is where judgment lives. In a workflow, judgment has already been encoded into the branches. In an agentic system, some of that judgment is being performed at runtime, inside guardrails, with access to tools, context, and fallback paths.

That is the foundation for understanding what an agentic AI platform actually is.

What is an agentic AI platform?

An agentic AI platform is the environment where AI agents are built, connected to tools and enterprise data, given goals and guardrails, observed in operation, and deployed into real business processes.

That definition sounds broad because the category is broad. But the distinction is still useful. A model can generate language. A framework can help structure prompts, memory, or tool use. A workflow engine can connect systems and automate steps. A platform is the layer that makes those pieces operational together.

This is where a lot of teams get tripped up. They assume an agent is just a model with a few tools attached. In practice, the moment an agent has to work across real systems, the surrounding platform starts doing most of the heavy lifting. It has to connect the agent to business data, control what the agent is allowed to access, trace what happened during execution, and provide the runtime where the system can act without becoming opaque or unsafe.

UiPath describes agentic AI through autonomy, planning, orchestration, and action across enterprise systems. Salesforce frames agentic AI around systems that can understand context, make decisions, and take action with less human direction. Automation Anywhere places emphasis on reasoning, acting, learning, and coordinating work across tools and processes. Put those views together, and a pattern becomes clear. An agentic AI platform is not just about generating answers. It is about creating the operating environment where agents can reason, retrieve context, use tools, take action, and remain governed.

In practical terms, a real agentic AI platform usually includes several layers working together.

It needs a reasoning layer so the agent can interpret a goal and decide what to do next. It needs tool use and execution so the agent can actually query systems, update records, trigger workflows, or escalate work. It needs context retrieval so the agent can pull the right information from enterprise systems instead of guessing. It needs memory or state so the system can preserve continuity across a task or process. It needs governance so permissions, policies, and safety boundaries are enforced. And it needs observability so teams can inspect what happened, why a decision was made, and where something went wrong.

That last part matters more than the market sometimes admits. The more dynamic the system becomes, the more important control and oversight become. Without them, “agentic” quickly turns into “unpredictable.”

A useful way to think about it is this: workflow automation tells software exactly what path to follow. An agentic AI platform gives software a goal, access to the right tools and context, and a bounded environment in which it can figure out the path.

Once the job involves runtime judgment, tool choice, recovery, and adaptation, you are no longer choosing only a model or only a workflow engine. You are choosing the environment in which that agent will operate.

Agentic AI platform vs workflow automation

This is the comparison that most teams need much earlier than they realise.

A workflow system and an agentic AI platform can both connect tools, trigger actions, and move work across systems. On the surface, they can look surprisingly similar. Under the surface, they are optimised for different kinds of work.

Workflow automation is optimised for predefined execution. You know the steps, the branch conditions, and the acceptable outputs in advance. The system is there to run that sequence reliably.

An agentic AI platform is optimised for runtime judgment. The system is given a goal, access to tools and context, and a bounded environment in which it can decide how to move toward the outcome.

The difference becomes easier to see when you compare the two side by side.

Dimension	Workflow automation	Agentic AI platform
Unit of execution	Predefined step or sequence	Goal oriented task
Decision making	Rule based branching	Runtime reasoning within guardrails
Path to outcome	Fixed or partially branched	Adaptive based on context and results
Tool use	Prewired in the flow	Chosen dynamically based on need
Context handling	Passed through predefined steps	Retrieved at runtime from relevant systems
Failure handling	Explicit fallback branches	Judgment, retry, escalation, or revision
Memory and state	Limited process state	Contextual state across the task
Governance	Process controls	Agent controls plus process controls
Best suited for	Predictable repeatable work	Ambiguous multistep operational work

Imagine a customer writes in asking why a refund has not been processed:

In a workflow system, you might classify the ticket, check refund status from one system, and route the case based on predefined conditions. That is useful and often exactly what the business needs.
In an agentic system, the software can go further. It can read the request, pull order history, inspect payment status, check whether the refund was blocked by policy or timing, look for prior communication, decide whether it has enough context to respond, and then either answer the customer, escalate to a human, or initiate the next operational step.

The difference is that the second system is designed to handle uncertainty as part of the job.

If the work is structured and repeatable, a workflow tool is often the right answer. If the work requires the system to interpret context, choose actions, and recover when the first path does not work, then a workflow engine alone usually starts to feel stretched.

This is also why the two categories should not be treated as opponents. Most production systems will use both. Workflows remain the backbone for deterministic execution. Agentic platforms add the reasoning layer needed when the next step cannot always be hardcoded in advance.

Why the distinction matters in production

A workflow can look impressive in a demo because the path is curated. The inputs are cleaner, the branches are known, and the edge cases are limited. Production is different. Requests arrive incomplete, tools fail, data is inconsistent, and the system has to decide whether to continue, retry, escalate, or stop.

You can keep layering rules on top, but the more the system has to interpret context at runtime, the more you need agent level controls.

That means visibility into why the system chose an action, guardrails on what it can access, and human checkpoints for higher risk decisions. Without that, teams end up with something that looks autonomous but is hard to trust.

This shows up quickly in customer operations, IT support, procurement, claims, and revenue workflows. These are all examples of enterprise AI agents in practice. They are hard because the system has to handle ambiguity without becoming reckless.

It changes what architecture you need, what controls you need, and what kind of system can actually survive past a polished demo.

What a real agentic AI platform should include

If a platform is serious about agentic AI, it should provide more than model access and workflow connectors.

At a minimum, it should let teams define goals, connect agents to tools and enterprise data, manage context, and trace decisions during execution. It should also support permissions, guardrails, and human review where needed.

A strong platform usually includes:

goal-based agent design
dynamic tool calling and action execution
context retrieval from enterprise systems
memory and state management
human in the loop controls
observability and decision tracing
security governance and permissions
deployment support for production operations

Some platforms will also support multi agent coordination, but that only matters once the fundamentals are already strong.

For teams comparing products in this space, this is also where it helps to study how different AI agent builders and platforms approach orchestration, context, governance, and deployment.

When you need workflow automation and when you need an agentic AI platform

Use workflow automation when the path is known, the rules are stable, and consistency matters more than runtime judgment.

Use an agentic AI platform when the system has to interpret goals, choose tools dynamically, work with changing context, and recover when the first path does not work.

In practice, most enterprise systems will use both. Workflows handle deterministic execution. Agentic platforms handle the parts of the job that cannot be fully hardcoded in advance.

If your team is crossing that line now, this is usually the point where trying a platform in a real business workflow tells you more than reading product definitions ever will. A hands on evaluation of something like the DronaHQ Agentic Platform can make that distinction much easier to judge.

Where this is headed next

The category is still messy, but the direction is becoming clearer.

Agents are moving from experiments into application patterns inside enterprise software. Gartner expects integrated AI agents to appear in 40% of enterprise applications by 2026, up from less than 5% in 2025. At the same time, weak projects will continue to get filtered out, especially when teams mistake AI flavored workflows for production ready agent systems.

The next wave of platforms will compete less on vague agent claims and more on orchestration, governance, observability, and how safely agents can operate across real systems.

Conclusion

Workflow automation is still one of the most useful categories in enterprise software. But it was built to execute known paths.

Agentic AI platforms matter when the path has to be figured out at runtime.

If the software you are building needs to reason, choose, adapt, and act across real systems, the distinction stops being semantic. It becomes architectural.

And once you are at that stage, the next decision is not just which model to use. It is which platform gives your agents the context, controls, and execution environment they need to operate in production.

Best n8n Alternatives for AI Automation (2026)

Gayatri Sachdeva — Thu, 12 Mar 2026 09:30:00 +0000

n8n gets recommended a lot when teams start exploring AI agents.

That makes sense. It is flexible, visual, developer friendly enough, and good at connecting tools. If your goal is to wire up APIs, move data between systems, or stand up an automation quickly, n8n is often one of the first tools people reach for.

But the conversation changes once teams try to build agents that need to do more than call a model and fire a webhook.

This is usually where the friction starts. You need agents that can retrieve the right context, interact with real business systems, trigger actions safely, and hold up once the workflow grows beyond a neat demo. You also start noticing that not every tool in this category solves the same problem.

Some are better at workflow automation. Some are better at AI orchestration. Some are better at giving agents a real operating environment.

That is why teams start looking at n8n alternatives.

This guide looks at the tools that come up most often in that search. The focus here is not generic automation software. It is platforms developers and technical teams evaluate when they want to build AI agents and agent driven workflows that can actually run inside production systems.

What is n8n?

n8n is an open source workflow automation platform used to build event-driven automation pipelines that connect APIs, SaaS tools, databases, and internal systems.

At its core is a visual node-based workflow editor where each step in a workflow, such as triggers, API calls, data transformations, or conditional logic, is represented as a node.

The platform is popular with developers because it combines the flexibility of code with the convenience of visual automation.

n8n key features

Visual workflow builder with reusable nodes and triggers
Extensive integrations across APIs SaaS tools and databases
Custom JavaScript logic inside automation workflows
Ability to self host workflows for control and security
Support for LLM integrations and AI workflow nodes

Because of this flexibility many developers describe n8n as a developer friendly alternative to Zapier.

Typical use cases include:

backend workflow automation
SaaS integrations
API orchestration
data processing pipelines
AI assisted automation workflows

n8n pricing

n8n offers both a cloud hosted version and a self hosted open source option. Pricing for the hosted version typically follows usage based plans depending on workflow executions and features.

n8n limitations

While n8n is powerful community reviews highlight several challenges that push teams to explore alternatives.

Users often highlight (G2) the flexibility and integration ecosystem but also mention that workflows can become harder to maintain as pipelines grow larger.

Common feedback mentioned across reviews and developer forums includes:

Workflows becoming complex as pipelines scale
Limited built in UI tools for internal dashboards
Additional engineering effort for production reliability
Debugging large workflows requiring deeper technical understanding

For many teams this becomes the point where exploring n8n alternatives starts to make sense.

Top n8n Alternatives (Shortlist)

If you are evaluating options quickly these platforms frequently appear in conversations around tools similar to n8n.
DronaHQ. AI agent platform for building agents that run across business systems.
Retool. Operational platform teams use to embed automation inside production workflows.
LangChain. Open source framework for building AI agent systems.
Make. Visual workflow engine for automations that may support agent driven flows.
Pipedream. Developer focused platform for API heavy automation and agent execution logic.

The sections below explore a broader list of platforms across automation AI orchestration and developer workflow tooling.

How We Evaluated n8n Alternatives

Each platform in this guide was evaluated against a consistent set of criteria. The goal here is not to pretend these tools are identical. They are not. Some are closer to workflow engines, some are clearly AI agent platforms, and some sit somewhere in between.

Each platform receives a score out of 10 based on the following factors:

How well it supports agent building and orchestration
How strong its workflow automation and execution layer is
How easily it connects to APIs databases and external tools
How usable it is for production facing agent workflows
How much control developers get over logic tools and extensibility
How clearly it fits real world agent use cases beyond demos

The scores are directional, not absolute. A higher score means the platform feels more complete and usable for teams building AI agents in real business environments.

Best n8n Alternatives in 2026

DronaHQ

Score: 9.1/10

Overview

DronaHQ is an AI agent platform for building agents that connect to APIs, databases, and business systems, then execute workflows inside real operations. It is best suited for teams that want agents to retrieve context, take actions, and run within governed production environments.

Key Features

Build AI agents connected to enterprise APIs and databases
Orchestrate agent workflows across operational systems
Add tools actions and execution logic to agents
Connect agents to governed business data and services
Run agents inside production business workflows

Cons

Requires clear system design for complex agent workflows
Better for operational agents than lightweight task automations

Pricing

Starts at $100 per user per month https://www.dronahq.com/agents/pricing/

LangChain

Score: 8.7/10

Overview

LangChain is an open source framework for building LLM powered applications and agents with fine grained control over orchestration. It is a common choice for teams that want to design agent behavior themselves and are comfortable assembling infrastructure around it.

Key Features

Framework for building LLM applications and agents
Tool calling and agent orchestration capabilities
Memory retrieval and vector database integrations
Large ecosystem of extensions and integrations

Cons

Requires engineering effort for production deployment
No built in visual workflow automation interface

Pricing

Open source

Zapier

Score: 7.9/10

Overview

Zapier is a broad automation platform that now includes AI features and agent style workflows. It is most useful for teams that want easy app to app automation with some AI assistance, rather than deep control over agent design or orchestration.

Key Features

Thousands of integrations across popular SaaS applications
Visual automation builder for triggers and actions
Large library of automation templates and examples
Built in AI features for repetitive task automation

Cons

Limited customization for developers and engineers
Pricing rises quickly at higher task volumes

Pricing

Starts at $19.99 per month

Make

Score: 8.2/10

Overview

Make is a visual automation platform built for teams that need more workflow control than basic no code tools usually offer. It works well for complex automation scenarios, though its agent capabilities still feel secondary to the workflow engine itself.

Key Features

Visual scenario builder with branching logic controls
Large integration ecosystem across SaaS tools
Real time monitoring of workflow executions
Flexible data transformation inside workflows

Cons

Complex workflows can become difficult to maintain
AI orchestration capabilities are still evolving

Pricing

Starts at $10 per month

Vellum

Score: 8.4/10

Overview

Vellum is an AI development platform focused on building, testing, and improving LLM applications and agents. It is strongest for teams that care about prompt orchestration, evaluation, and reliability, and less focused on broad workflow automation across business systems.

Key Features

Prompt management and versioning for AI workflows
Multi model orchestration across LLM providers
Evaluation pipelines for testing AI outputs
Collaboration tools for AI engineering teams

Cons

Limited traditional workflow automation capabilities
Focused mainly on AI application development

Pricing

Custom pricing

Flowise

Score: 7.8/10

Overview

Flowise is an open source visual builder for assembling LLM workflows and agent style systems. It appeals to developers who want a more visual way to experiment with AI orchestration without fully giving up the flexibility of the underlying frameworks.

Key Features

Visual builder for LLM workflows and agents
LangChain integrations for AI orchestration flows
Vector database support for retrieval pipelines
Open source deployment and customization options

Cons

Limited enterprise governance and monitoring features
Requires technical knowledge to deploy and manage

Pricing

Open source

Relevance AI

Score: 7.7/10

Overview

Relevance AI is an AI agent platform focused on operational automation and structured business workflows. It is more relevant for teams pursuing agent led business processes than for teams looking for a general purpose API automation engine.

Key Features

AI agent platform for operational automation
Workflow pipelines for processing structured data
LLM integrations across multiple model providers
Tools for deploying agents into workflows

Cons

Smaller ecosystem than major automation platforms
Less suited for general API automation workflows

Pricing

Starts at $19 per month

Lindy

Score: 7.4/10

Overview

Lindy is built around AI assistants that automate common business tasks across connected applications. It works best for teams that want fast assistant style automations, though it offers less depth for developers building more structured agent systems.

Key Features

AI assistant automation for business workflows
Integrations with popular SaaS applications
Templates for common automation tasks

Cons

Limited customization for developer driven workflows
Not designed for complex backend automation systems

Pricing

Starts at $29 per month

Pipedream

Score: 8.3/10

Overview

Pipedream is a developer focused integration platform for event driven workflows, custom logic, and API heavy automation. It is a strong option for technical teams that want code level control, though it is less purpose built for agent design than agent first platforms.

Key Features

Serverless workflow execution for backend automation
Support for Node.js and Python code steps
Large catalog of API integrations and triggers
Strong debugging tools for developers

Cons

Requires programming knowledge for most workflows
No built in internal application builder

Pricing

Starts at $19 per month

Retool

Score: 8.8/10

Overview

Retool is best known for internal software and operational workflows, but it also appears in n8n alternative searches because teams use it to wrap logic and automation in production facing systems. It is less agent first than AI native platforms, but strong for operational control.

Key Features

Visual builder for internal dashboards and admin tools
Database and API integrations for operational apps
Workflow automation features for backend processes
Role based access control for enterprise teams

Cons

Automation depth trails dedicated workflow platforms
Pricing increases as team size grows

Pricing

Starts at $10 per user per month

Comparison Table

Platform	Score	Workflow Automation	AI Integration	Internal Tools	Developer Flexibility
DronaHQ	9.1	Strong	Strong	Strong	Strong
LangChain	8.7	Moderate	Strong	Limited	Strong
Zapier	7.9	Strong	Moderate	Limited	Basic
Make	8.2	Strong	Moderate	Limited	Moderate
Vellum	8.4	Limited	Strong	Limited	Moderate
Flowise	7.8	Moderate	Strong	Limited	Moderate
Relevance AI	7.7	Moderate	Strong	Limited	Moderate
Lindy	7.4	Moderate	Moderate	Limited	Basic
Pipedream	8.3	Strong	Moderate	Limited	Strong
Retool	8.8	Moderate	Moderate	Strong	Strong

Conclusion

The ecosystem around automation and AI workflows is evolving quickly. While n8n remains a powerful automation platform many teams evaluate alternatives as their systems grow more complex.

The right choice depends on what you are actually trying to build. Some teams need stronger workflow orchestration. Others need deeper AI tooling. And many need a platform that can support both automation logic and operational interfaces in the same place.

How teams build internal apps in the Vibe Coding era

Gayatri Sachdeva — Tue, 10 Mar 2026 13:53:43 +0000

Building internal apps has been one of the most repeated yet underestimated engineering activities inside modern companies. Internal apps quietly run most modern organizations. Finance approvals, vendor onboarding, pricing controls, inventory adjustments, compliance workflows, and operational dashboards all depend on them. Yet many teams still build internal tools using the same processes, sprint structures, and frontend assembly logic designed for customer-facing products.

That mismatch is expensive.

Teams routinely allocate senior engineering bandwidth to rebuild CRUD screens, wire permissions manually, configure environments from scratch, and stitch workflows that follow predictable patterns. Internal apps are treated as side projects, even though they influence revenue recognition, compliance posture, operational speed, and decision accuracy.

In 2026, that model of internal app development is breaking down. The shift is not about typing a prompt and getting an app. It is about rethinking how internal software should be assembled in the first place.

How teams traditionally build internal apps

For most engineering teams, building internal apps or creating internal tools historically followed one of three paths.

The first path is full-stack custom development. A React or similar frontend is created, backend APIs are built or extended, and UI logic is implemented manually. Role-based access control is coded explicitly. Environments are configured across staging and production. Version control and deployment pipelines mirror customer product workflows. This approach offers maximum flexibility, but it consumes significant frontend bandwidth for applications that often follow predictable CRUD and workflow patterns.

The second path is visual low-code builders. These platforms accelerate UI assembly with pre-built components and connectors. They reduce boilerplate, but can introduce rigidity if complex workflows or custom logic are required. Governance varies by platform, and extensibility may depend on how well the system supports code-level control and secure integration.

For teams exploring this category in depth, it is useful to review how modern platforms compare in flexibility and control. Read more >

The third path, more recently, is AI code generation. Teams describe an internal tool, and an AI model scaffolds a frontend and sometimes backend code. This approach can produce rapid prototypes. However, unstructured generation frequently raises questions about maintainability, environment alignment, permission modeling, and lifecycle management. Generated code may require significant cleanup before it can safely move into production. For a broader view of how AI app builders are evolving, see.

Across all three paths, one pattern remains consistent. Engineering effort is repeatedly spent on interface assembly, permission wiring, workflow states, and environment setup for tools that share similar structural characteristics.

What teams are misunderstanding about internal apps today

The most common misunderstanding about building internal apps is that they are lightweight software.

Because they are not customer-facing, teams assume they can tolerate informal architecture, looser permission models, and less disciplined lifecycle management. In reality, internal tools often have deeper operational impact than customer features. They influence payouts, inventory movements, compliance reporting, and executive decisions.

A second misunderstanding is that the hard part of internal app development is business logic. In most organizations, the backend systems already exist. APIs are available. Databases are structured. The recurring friction lies in assembling the interface layer repeatedly and aligning it with permissions and workflows.

A third misunderstanding is that AI generation alone solves the problem. Generating UI code reduces initial effort, but it does not automatically solve environment isolation, audit logging, structured RBAC, or long-term maintainability. Without those elements, internal apps accumulate hidden operational risk.

When teams correct these assumptions, they start evaluating internal app development differently.

Why the internal app model needed to change

Internal tools differ from customer-facing software in several important ways.

They often center around structured data, defined workflows, and controlled access. Many follow recurring patterns: dashboards, tables, filters, approvals, status transitions, exports, and audit trails. Despite this repetition, teams frequently rebuild these patterns from scratch.

The bottleneck in internal app development is rarely business logic. Backend services, APIs, and databases already exist in most organizations. The friction lies in assembling the interface, binding it to data securely, modeling permissions accurately, and deploying it within governed environments.

AI-based scaffolding addressed part of the repetition, but it did not fully solve lifecycle challenges. Generating a screen is different from maintaining a governed internal system with environment separation, audit logging, version control, and extensibility.

This gap created space for a new model.

The emerging model of internal app development

In 2026, many teams building internal apps are shifting toward intent-first internal app development models.

Intent-first development begins with describing operational requirements rather than manually assembling components. Instead of starting with an empty UI canvas, teams articulate what the internal tool must do: which data it should surface, which roles can access it, which workflow states exist, and how actions should trigger system changes.

From that intent, structured interfaces and workflow logic are generated within a governed platform. The output is not freeform code dumping. It is a structured representation that aligns with the platform’s component hierarchy, data bindings, and permission framework.

Vibe coding is a term often used to describe this approach. In the context of internal apps, vibe coding refers to AI-assisted, intent-driven interface and workflow generation inside a system that enforces environment separation, role-based access control, and deployment standards. For a deeper breakdown of this approach, read more.

The emphasis is on structured generation within boundaries, not unbounded code output. Teams evaluating this space often compare platforms based on how well they balance AI assistance with governance.

What building internal apps looks like with this model

Consider a finance approval tool.

The team defines the requirement: a request submission form, a review dashboard for managers, multi-step approval states, and restricted visibility based on role.

Using an intent-first approach, the base interface is generated with forms, tables, filters, and status indicators aligned to the defined schema. The tool binds directly to existing APIs or databases. Role-based access is configured through explicit permission layers rather than ad hoc conditionals. Workflow states are modeled as structured transitions rather than scattered logic.

Before deployment, the app is tested in a staging environment. Audit logging is enabled to track state changes and approvals. Version control ensures that updates can be rolled back if needed. Only after validation does the app move to production.

The lifecycle includes generation, refinement, validation, deployment, and iteration. AI assists the repetitive assembly, but governance and architectural decisions remain deliberate.

This is where the emerging model differs from simple scaffolding.

Traditional development vs the emerging model

Traditional full-stack development offers deep flexibility and direct control, but it requires significant engineering effort for repetitive UI and workflow patterns.

Pure AI scaffolding reduces initial build time, yet may introduce ambiguity around permissions, environment management, and long-term maintainability.

The emerging model combines structured AI assistance with platform-level governance. It reduces repetitive interface work while preserving environment isolation, role-based access control, extensibility through code, and auditability.

The difference is operational. It determines whether internal apps remain manageable as organizational complexity grows.

How engineering teams evaluate internal app platforms in 2026

When evaluating platforms for building internal apps today, engineering leaders focus less on demo speed and more on system characteristics.

They assess whether the platform supports secure connectivity to existing APIs and databases. They examine how deeply role-based access can be configured. They review environment separation across development, staging, and production. They verify audit logging and compliance capabilities. They test extensibility, including the ability to inject custom JavaScript or Python where required.

AI-assisted generation is considered valuable only if it exists inside this broader governance framework.

Platforms that combine structured AI generation with enterprise-grade controls are increasingly becoming the operational layer for internal tools.

Where vibe coding fits

Within this broader transformation, vibe coding serves as a productivity layer rather than a replacement for engineering judgment.

It reduces repetitive UI assembly and workflow scaffolding. It accelerates the initial translation from requirement to structured interface. At the same time, it operates within systems that support role-based access, environment management, secure integration, and maintainable architecture.

In platforms such as DronaHQ, vibe coding exists inside a developer-oriented environment that includes secure connectivity, RBAC, audit logs, hosting controls, and code extensibility. The AI layer assists with assembly, while the platform enforces structure.

This balance distinguishes structured internal app development from unbounded AI generation.

What building internal apps will look like next

Internal apps and internal tools are becoming the operational control plane of modern organizations. As companies scale, the complexity of approvals, data visibility, compliance workflows, and cross-team coordination increases. Internal tools cannot remain informal artifacts assembled ad hoc.

The next phase of internal app development will prioritize structured generation, governed environments, secure integration, and long-term maintainability as first principles rather than afterthoughts. AI will continue to reduce repetitive assembly work, but the competitive advantage will lie in how well teams combine that assistance with architectural discipline.

Teams that treat internal apps as strategic infrastructure rather than secondary utilities will move faster operationally, reduce hidden maintenance cost, and respond more confidently to growth and regulatory change.

The shift in how teams build internal apps is not about replacing developers. It is about elevating internal app development from repetitive interface construction to structured system design supported by intelligent tooling.

5 Workflows where ecommerce AI agents beat generic chatbots

Gayatri Sachdeva — Tue, 10 Mar 2026 12:42:55 +0000

AI in ecommerce has moved beyond simple chat widgets that answer FAQs. Today, ecommerce AI agents act as goal-oriented assistants that can plan, reason, and take actions across your stack to drive revenue, reduce cart abandonment, and resolve CX bottlenecks in ways a traditional ecommerce chatbot cannot.

Unlike a basic ecommerce chatbot that reacts to user questions inside a chat window, ecommerce AI agents operate across the entire customer journey — from product discovery and checkout to returns and post-purchase operations.

Chatbots vs AI Agents in E-commerce: What’s the Real Difference?

In ecommerce, a chatbot is typically a rules-based or flow-based assistant that responds to customer questions using predefined scripts or basic NLP. An ecommerce chatbot may trigger simple actions like creating a support ticket or sharing a link, but it generally operates within a fixed decision tree.

An ecommerce AI agent, by contrast, is a goal-driven system that understands intent and context, reasons over real-time data, and executes multi-step workflows across systems such as your catalog, OMS, CRM, marketing platform, and inventory tools. The objective is not just to answer a question, but to achieve a measurable business outcome such as higher conversion, lower returns, faster resolution time, or improved customer lifetime value.

Chatbots primarily operate inside a conversation. They respond to prompts and escalate when flows break. Ecommerce AI agents observe behavior across sessions, anticipate needs, and coordinate actions across systems — adjusting recommendations, recovering carts, resolving refunds, and updating backend systems without waiting for a perfectly phrased question.

Dimension	Ecommerce Chatbot	Ecommerce AI Agent
Primary role	Answers FAQs and handles scripted support flows.	Pursues business goals such as higher conversion, lower abandonment, and faster refunds.
Context depth	Limited to current session and a few attributes.	Uses browsing behavior, purchase history, inventory, and logistics data to personalize decisions.
Actions	Shares information and collects inputs.	Edits orders, checks stock, launches campaigns, triggers returns, and coordinates systems.
Learning	Improves through manual flow edits.	Improves based on feedback, behavioral data, and performance outcomes.
Business impact	Reduces support workload.	Directly influences revenue, AOV, cart recovery, CSAT, and cost-to-serve.

Workflow 1: Guided Product Discovery and Personal Shopping

Most ecommerce chatbots support basic discovery. They answer questions like return policies or shipping zones and may run a simple quiz. However, they struggle when a shopper has layered requirements involving budget, size, availability, and delivery windows.

Ecommerce AI agents for guided selling act more like personal shoppers. They can:

• Read product metadata, reviews, and real-time availability to recommend relevant options.
• Combine browsing behavior and purchase history to personalize bundles or cross-sells.
• Engage proactively on product and category pages based on signals, not just chat prompts.

For ecommerce brands focused on improving conversion rate and AOV, this is one of the clearest use cases for AI agents in ecommerce.

Workflow 2: Cart Recovery and Checkout Rescue

Cart abandonment remains a major ecommerce challenge. A traditional ecommerce chatbot may show a generic pop-up or send a reminder email, but these flows rarely adapt to the reason for abandonment.

Ecommerce AI agents can treat each cart as a distinct scenario by:

• Detecting real-time friction signals such as payment retries or device switching.
• Choosing the appropriate recovery channel based on user history.
• Resolving blockers directly, including payment assistance or contextual incentives within margin guardrails.

Instead of static reminder flows, ecommerce AI agents personalize recovery strategy per customer and per context.

Workflow 3: Customer Support That Actually Resolves Issues

An ecommerce chatbot works well for repetitive questions such as order tracking. It often fails when the issue involves damaged goods, multi-SKU exchanges, or policy exceptions.

An ecommerce AI support agent can:

• Pull order data, shipment history, prior tickets, and images.
• Apply policy logic within predefined guardrails.
• Execute decisions across systems, including refunds, replacements, and label generation.

The shift from chatbot-based deflection to agent-based resolution allows ecommerce brands to reduce handle time and improve first-contact resolution.

Workflow 4: Returns, Refunds, and Exchanges

Most ecommerce chatbots treat returns as form collection. This adds manual review and delays.

An ecommerce AI agent can manage returns operationally:

• Validate eligibility based on SKU, time, and customer history.
• Recommend refund, exchange, or store credit aligned with business rules.
• Update OMS, inventory, and warehouse systems automatically.

Over time, these ecommerce AI agents can also detect patterns driving returns and surface operational insights to merchandising and supply chain teams.

Workflow 5: Inventory-Aware Promises and Post-Purchase Operations

Ecommerce chatbots typically provide static answers about availability and shipping.

Inventory-aware ecommerce AI agents coordinate promises with real-time inventory and logistics data. They can:

• Check availability across warehouses and stores before committing delivery windows.
• Suggest substitutes when stock is low.
• Trigger internal workflows during disruptions.

This is where ecommerce AI agents extend beyond support and influence pricing, promotions, and fulfillment decisions.

When Is an Ecommerce Chatbot Still Enough?

For early-stage ecommerce brands with small catalogs and limited operational complexity, a well-designed ecommerce chatbot can handle FAQs and basic support efficiently.

Chatbots can also function as a triage layer, routing intents to specialized ecommerce AI agents or human teams. The upgrade from ecommerce chatbot to ecommerce AI agent typically becomes necessary when static flows can no longer manage rising operational complexity.

How to Upgrade from an Ecommerce Chatbot to Ecommerce AI Agents

Transitioning from chatbot automation to ecommerce AI agents does not require replacing your stack. It requires layering intelligence across workflows.

Start by identifying high-impact workflows:

• Guided selling and product discovery
• Cart recovery
• Customer support
• Returns and exchanges
• Post-purchase and fulfillment coordination

Then integrate agents with core systems such as PIM, OMS, CRM, ticketing, and marketing tools. Define clear decision thresholds and escalation rules.

Over time, ecommerce brands can evolve from a single AI support agent to a coordinated ecosystem of ecommerce AI agents operating across revenue, operations, and CX.

Build Ecommerce AI Agents with DronaHQ Agentic Platform

If you want to move beyond a traditional ecommerce chatbot and deploy production-ready ecommerce AI agents, you do not need to rebuild infrastructure. With an agentic platform like DronaHQ, you can orchestrate guided selling agents, cart recovery agents, support agents, and operations agents on top of your existing stack and start validating impact quickly.

Build an eCommerce support AI agent in 45 minutes using DronaHQ

Gayatri Sachdeva — Tue, 10 Mar 2026 09:34:17 +0000

To build this agent, set up your free account.

Customer expectations have shifted. People want instant answers, accurate information, and seamless resolution across chat, email, and voice. Conversational AI for customer service has improved response speed; however, many deployments still stop at scripted replies and static flows.

AI agents for customer service represent the next stage. These AI customer service agents combine language understanding with structured access to your systems.

They do not simply respond to queries. They interpret intent, retrieve context, take action across tools, and escalate to humans when judgment is required. In practical terms, the model looks like this:

Customer → AI agent → system orchestration → human escalation if needed.

A customer surfaces a question. The agent understands the intent, checks relevant systems such as CRM, ERP, or billing, completes permitted actions within policy, logs the interaction, and escalates only when confidence is low or the case falls outside defined boundaries. This is the foundation of AI-powered customer support.

No-code AI agents for customer service, such as those built with DronaHQ Agents, allow CX teams to deploy these capabilities without building orchestration logic from scratch.

Why support teams are hitting a ceiling without AI agents

Support volumes continue to grow while customers expect 24/7 coverage and consistent answers across channels. Contact centres face pressure to reduce cost per contact while maintaining service quality.

Human-only teams struggle to scale predictably. Wait times increase during peaks. Knowledge inconsistencies surface across agents. Training cycles become longer and more expensive.

AI agents for customer support address these constraints by offering structured, always-on handling of repetitive and policy-bound interactions. This is where AI customer support automation moves beyond answering questions and begins coordinating actions.

For example: Order tracking
When a customer asks, “Where is my order?” The agent checks the ERP for shipment status, confirms the carrier tracking ID, updates the CRM timeline, logs the interaction in Freshdesk, and sends the tracking link via email. If the shipment shows a delay beyond SLA, it escalates with context to a human agent.

This flow reduces response time while preserving escalation pathways for exceptions.

What is a customer service AI agent?

Conversational AI vs traditional chatbots

A customer service AI agent is an LLM-powered virtual agent that understands intent, retrieves relevant knowledge, and can take structured actions or escalate appropriately. It combines conversational AI for customer service with system integrations and policy enforcement.

Traditional chatbots rely on predefined flows and keyword rules. AI customer service agents interpret language more flexibly and operate within guardrails that allow action across systems.

Capability	Legacy Chatbot	AI Customer Service Agent
Logic	Rule based	LLM + workflow orchestration
Autonomy	FAQ replies	Executes actions within policy
System access	Limited	Fully integrated within boundaries
Resolution scope	Single step	End to end task completion

AI agents vs chatbots: Key differences that matter for CX

The comparison of AI agents vs chatbots is not about interface, but about capability and impact.

A chatbot may answer “How do I return an item?” with instructions. An AI agent for customer service can verify the order, check return eligibility, generate a return label, update the order system, notify the warehouse, and confirm via email.

Example: Refund processing

A customer requests a refund. The agent checks the order in ERP, verifies the return window policy, confirms payment method in billing, processes a refund below a defined threshold, updates the CRM, and sends confirmation. If the refund exceeds policy limits, it prepares a summary and routes it to a human.

The difference lies in the agent’s ability to reason within policy and act accordingly.

How a customer-facing AI agent actually works

A customer-facing AI agent begins at the first touchpoint. It receives a query in chat, email, or voice. It identifies intent, retrieves necessary context, and determines what actions are allowed.

If the case fits within predefined rules and confidence thresholds, it proceeds to execute structured actions across systems. If ambiguity or risk is detected, it escalates to a human agent with full context attached.

Example: Subscription upgrade

A user asks to upgrade their plan. The agent checks current subscription in CRM, validates pricing rules, updates billing, modifies entitlements in the product database, logs the change in the ticketing tool, and confirms the upgrade. If payment fails, it escalates with transaction details.

This orchestration model ensures continuity rather than isolated responses.

How AI customer service agents work (Conversational AI + agentic AI stack)

Modern AI customer service agents operate through layered intelligence.

Understanding and routing

The agent interprets user intent, sentiment, and urgency. It routes tickets or initiates workflows based on confidence levels. This layer powers ai customer support automation.

Example: Password reset
A user says they cannot log in. The agent verifies identity through predefined checks, triggers a secure password reset workflow, updates the ticket status, and confirms completion. If identity verification fails, it escalates.

Retrieving knowledge and grounding

The agent references approved documentation and knowledge bases to generate accurate, grounded responses.

Acting across tools

Agentic AI for customer service enables secure API calls across CRM, ERP, billing, and ticketing systems.

Example: Shipping address update
A customer requests a delivery address change. The agent checks shipment status in ERP, confirms eligibility for modification, updates the address in the order system, syncs the change to CRM, logs the action in Freshdesk, and confirms to the customer.

15 high-impact AI agents for customer service across industries

Below are 15 well-defined AI agents for customer service. Each example reflects a true AI agent that understands intent, accesses live systems, reasons within policy, and executes actions across tools rather than simply replying with scripted answers.

Ecommerce

Order resolution agent Handles tracking, shipment delays, and carrier updates. Checks ERP, retrieves live tracking, updates CRM timeline, and proactively notifies customers if SLA risk is detected.
Returns and refund agent Validates eligibility against policy, generates return labels, processes refunds below threshold via payments API, updates ERP and CRM, and escalates exceptions.
Post-purchase modification agent Updates shipping addresses or delivery windows when eligible, synchronizes ERP and logistics systems, and confirms changes via email.

SaaS

Subscription lifecycle agent Manages plan upgrades, downgrades, renewals, and proration. Connects CRM, billing platform, and product entitlement systems before confirming changes.
Account access agent Handles password resets, MFA issues, and role changes by validating identity and triggering secure workflows in IAM systems.
Usage intelligence agent Monitors product usage, identifies churn risk signals, and proactively notifies customers about overages or optimization opportunities.

Banking and fintech

Transaction inquiry agent Retrieves transaction history, explains charges using grounded policy data, and escalates fraud signals when anomaly thresholds are triggered.
Dispute initiation agent Collects required details, creates structured dispute records, updates case management systems, and informs customers of next steps.
Card services agent Handles card activation, temporary blocks, and replacement requests through secure verification and backend updates.

Telecom

Outage response agent Correlates location data with network status, informs customers of active outages, creates service tickets when needed, and updates CRM.
Plan migration agent Recommends eligible plans, updates billing systems, modifies provisioning records, and confirms new entitlements.

Travel and hospitality

Booking modification agent Checks fare rules, rebooks flights or rooms within policy, updates reservation systems, and sends updated itineraries.
Cancellation and refund agent Validates eligibility, processes refunds through payment gateways, updates booking systems, and triggers confirmation workflows.

Insurance and healthcare

Claims intake agent Collects structured claim data, validates policy coverage, creates case files in claims systems, and notifies customers of documentation gaps.
Appointment coordination agent Schedules, reschedules, and confirms appointments by integrating with provider systems and sending reminders across channels.

These agents move beyond scripted conversations. They combine conversational AI with structured orchestration across CRM, ERP, billing, and ticketing systems, forming the backbone of AI customer support automation at scale.

If this orchestration model resonates and you are exploring how to implement it in your own support stack, review how structured AI agents can be deployed inside a governed environment with DronaHQ Agents.

How to cut support costs with automation without wrecking CX

Leaders exploring how to reduce support costs with AI focus on staffing, peak coverage, handle time, and quality assurance.

AI-powered customer support reduces repetitive ticket load and shortens response times. When 25 to 40 percent of routine inquiries are handled by agents within policy, cost per contact declines while human agents focus on nuanced cases.

The key is measured deployment. Human-in-the-loop customer service AI ensures oversight for edge cases and preserves brand quality.

AI support maturity model

Support teams typically evolve through three maturity levels. Each stage carries different risks, data requirements, and next steps.

Level 1: FAQ assistant
Handles basic queries using a knowledge base.
Risk: Hallucinations if grounding is weak.
Data requirement: Clean, updated documentation.
Next step: Add structured intent classification and analytics tracking.

Level 2: Transactional agent (orders, refunds, resets)
Executes single-system actions within policy.
Risk: Policy misconfiguration or incorrect threshold logic.
Data requirement: API access to CRM, ERP, billing with audit logging.
Next step: Introduce multi-step workflows and human approval triggers.

Level 3: Agentic orchestrator (multi-system workflows, proactive communication)
Coordinates actions across CRM, ERP, ticketing, and messaging channels.
Risk: Over-automation without clear escalation rules.
Data requirement: Unified customer context, role-based permissions, monitoring dashboards.
Next step: Add proactive notifications and continuous optimization based on CX metrics.

From pilot to agentic AI contact center

A practical implementation roadmap:

Audit top intents and select high-volume, low-risk journeys.
Clean knowledge sources and define response policies.
Launch a customer service AI agent for FAQ and simple transaction handling.
Introduce agent assist for human teams.
Expand into multi-system workflows within defined guardrails.

At each stage, measure containment, resolution time, and customer satisfaction.

Launch your first AI support agent

Customer-facing AI agents deliver the most value when they are deployed in a controlled, measurable pilot. Start with a high-volume journey such as order tracking or refunds, define clear policy thresholds, and connect your CRM, ERP, and helpdesk systems.

You can launch a pilot AI agent in a no-code builder and move from FAQ handling to transactional orchestration in stages. Explore how to get started with a governed, production-ready approach through DronaHQ Agents.

Building internal tools in 2026: vibe‑coded apps

Gayatri Sachdeva — Tue, 17 Feb 2026 12:40:11 +0000

What internal tools are (and why 2026 feels different)

Internal tools (or internal apps) are the behind‑the‑scenes applications your teams use to run operations: admin panels, approval workflows, back‑office dashboards, vendor portals, internal CRMs, and countless one‑off utilities.

In 2026, the way these tools are built is shifting from hand‑coded dashboards and classic no‑code builders toward AI‑assisted “vibe coding” where you describe what you want and the system scaffolds an app for you.

The opportunity is huge: faster iteration, more people empowered to build, and dramatically lower costs for many kinds of internal apps.

The risk is also real: fragile logic hidden in prompts, security gaps, and a growing tangle of half‑finished tools if you treat vibe coding as magic instead of a new layer in your stack.

A short history of internal tools (and what each era got right and wrong)

1. The legacy era: spreadsheets, scripts, and ad‑hoc admin panels

For years, internal tools were either spreadsheets plus email, or custom admin panels hacked together by a few engineers.

These approaches offered maximum flexibility but came with obvious pains: single‑developer dependency, slow iteration, and fragile glue scripts between systems.

As companies scaled, the cost of every “small” internal request became unsustainable: product and ops teams waited weeks or months for even basic changes to forms, workflows, and dashboards.

This bottleneck is the backdrop that made no‑code internal tool builders so attractive.

2. The internal tool builder / no‑code era

Around the mid‑2020s, platforms focused on building internal tools—visual frontends over your databases, APIs, and SaaS apps—went mainstream.

Tools like Appsmith, Softr, ToolJet, WeWeb, Glide, and others gave teams drag‑and‑drop UIs, ready‑made components, and connectors to popular data sources so they could ship CRUD apps and dashboards quickly.

The upside was dramatic: faster delivery, more “citizen developers,” and a common pattern for typical internal apps (tables, filters, forms, workflows).

The downside emerged later: app sprawl, inconsistent governance, and limits around very complex logic, performance, and cross‑system integrity that still needed engineers.

3. The AI / vibe coding era

In the last couple of years, a new layer appeared on top of both coding and visual builders: vibe coding.

Instead of manually assembling every component, you describe your internal tool in natural language (“a vendor onboarding tool with approvals, document uploads, and Slack notifications”), and an AI‑powered system generates data models, screens, and workflows you can then refine.

This pattern shows up across the ecosystem: internal‑tool‑focused builders adding AI agents, standalone AI app builders, and LLM‑powered code generators you can run alongside your existing stack.

Vibe coding is not a replacement for internal tool builders or traditional engineering; it’s a new scaffold that speeds you from “blank page” to “something testable” far faster than previous eras.

4. 2026 reality: hybrid stacks, not silver bullets

Most teams building serious internal tools in 2026 use a hybrid stack rather than betting everything on one paradigm.

They combine classic code for core systems, visual/low‑code builders for most day‑to‑day internal apps, and vibe coding for rapid prototyping and scaffolding that gets hardened in visual builders or production code.

The mindset shift is key: you’re not choosing “code vs no‑code vs vibe coding” once; you’re choosing the right combination per tool, based on stakes, complexity, and lifecycle.

The rest of this guide is about making that choice explicit instead of accidental.

Framework: when to use code, no‑code, or vibe coding for internal apps

There’s no universal best approach, but you can choose confidently if you classify internal tools by stakes and change‑rate.

Classifying your internal tools

Think in three rough buckets:

Low‑stakes utilities: low user count, limited risk, can tolerate occasional quirks (e.g., small ops helpers, one‑off data clean‑up tools).
Medium‑stakes tools: support daily operations, but failures are recoverable and impacts are local (e.g., internal dashboards, basic approvals, content management UIs).
High‑stakes systems: critical to revenue, compliance, or security; failures are costly or reputationally damaging (e.g., financial reconciliation, access management, regulatory reporting).

Now map these against your tool types.

Choosing the right build approach

Tool type	Best for	Avoid for	Role in 2026 internal stacks
Classic code frameworks	High‑stakes, deeply integrated, long‑lived systems with complex logic and strict performance requirements	Rapid experiments, short‑lived utilities, workflows you’re still discovering	Backbone for critical flows and shared services that multiple internal tools depend on.
Visual / low-code internal tool builders	Medium‑stakes CRUD apps, dashboards, admin panels, and workflows over existing data and APIs	Extremely unusual logic, extreme scale/performance, bleeding‑edge tech stacks	Main workhorse for most internal apps, especially where speed and maintainability for ops teams matter.
Vibe coding / AI app builders	Low‑ to medium‑stakes experiments, prototypes, and self‑contained utilities; first drafts of larger internal apps	High‑stakes systems, multi‑system critical workflows, anything requiring strict audit/compliance	Scaffolding and acceleration layer: generate structure fast, then harden in a visual builder or codebase.

As a simple rule of thumb: the higher the stakes and the longer the expected life of an internal tool, the more you should bias toward visual builders plus explicit engineering, and the more carefully you should constrain vibe coding to design and early drafts.

How to build internal tools in 2026: a practical playbook

This is a concrete path you can follow for your next internal app project.

Step 1: Map workflows, data, and owners

Start by writing down:

What the tool needs to do (workflows, inputs, outputs).
Where the data lives today (SaaS tools, databases, spreadsheets, APIs).
Who owns the process and will be accountable for the tool’s behaviour.

This is not about over‑engineering; it’s about avoiding the common trap of letting the builder dictate your process instead of the other way around.

Even a quick diagram or text sketch of “systems → internal app → users” gives you clarity before you touch any platform.

Step 2: Classify the tool’s stakes and lifecycle

Decide whether the tool you’re building is likely to be:

A short‑lived experiment (weeks to a few months).
A medium‑term ops tool (months to a couple of years).
A long‑term core system (years, shared across teams).

If it’s short‑lived and low‑stakes, you can lean heavily on vibe coding and faster, more opinionated internal tool builders.

If it’s long‑term and high‑stakes, plan for a more deliberate architecture and treat AI‑assisted generation as a helper, not the source of truth.

Step 3: Pick your stack: code, visual builder, vibe coding (or mix)

Use your classification to choose:

A visual / internal‑tool builder as the default for most medium‑stakes internal apps (e.g., tools similar to DronaHQ’s low-code app builder, Appsmith, Softr, WeWeb, ToolJet, Glide, Retool).
Direct engineering (frameworks, custom backends) where you need full control, performance, or deep integration.
Vibe coding tools or AI capabilities inside your builder to generate initial data models, screens, and workflows.

Many 2026 “how to build internal tools” guides recommend choosing a single platform and going all‑in.

In practice, high‑performing teams pick a default builder for most tools, then layer vibe coding and custom code where it actually helps.

Step 4: Apply vibe coding safely

Vibe coding is most powerful when you treat it as scaffolding, not as the final implementation.

Use AI to:

Generate first‑pass data models (“Tickets have status, priority, assignee, SLA, tags…”).
Generate screens for common patterns (lists, detail views, filters, forms, approval flows).
Suggest simple automations (notifications, escalations, basic branching).

Then deliberately review and harden:

Check auth and access control (who can see or edit what).
Verify that queries and filters make sense and won’t break under real data.
Replace opaque prompt‑based logic with explicit, testable rules where it matters.

A big theme in “why vibe coding can break your critical internal tools” essays is invisible coupling between prompts and behaviour.

By turning AI‑generated behaviours into transparent configuration or code, you get the best of both speed and maintainability.

Step 5: Plan governance and maintenance from day one

Internal tools rarely stay “side projects” for long; they become embedded in processes.

That’s why modern guides emphasize governance as much as building: you need clear ownership, versioning, and guardrails once you have dozens or hundreds of internal apps.

At minimum, decide:

Who owns each internal app (business owner + technical owner).
How changes are requested, reviewed, and rolled out (environments, testing).
How access is managed (RBAC, audit logs, offboarding).
How you’ll observe and debug issues (logs, alerts, error reporting).

If vibe coding is part of your stack, include rules for when it is allowed (e.g., prototypes, non‑critical tools) and when changes must go through review in a visual builder or code repo.

This is the difference between “AI‑powered chaos” and a sustainable internal tools practice.

Where vibe coding works for internal tools—and where it fails

Vibe coding is particularly helpful for:

Greenfield prototypes where you’re still discovering the workflow.
Utilities that wrap a single system (e.g., a quick admin UI for a new SaaS).
Teams that know their process well but struggle to translate it into components and queries.

It tends to fail or cause trouble when:

You’re dealing with multiple critical systems and complex transactional logic.
You need strong, auditable control over who can do what and when.
You treat the generated app as “done” instead of as a first draft to refine.

The most forward‑looking content on internal tools in 2026 is converging on the same theme: vibe coding should supercharge your internal tool builders and engineering practice, not bypass them.

Use AI to remove the blank‑page and boilerplate pain, then rely on visual builders and code to make your internal apps understandable, governable, and robust.

Agentic AI for your neighbourhood pharmacy

Gayatri Sachdeva — Thu, 29 Jan 2026 13:03:16 +0000

The neighbourhood pharmacy has always been a cornerstone of the community. But in a world where Amazon delivers in hours, and big-box retailers compete on price, the neighbourhood chemist is facing a crisis of relevance.

The neighbourhood pharmacy is at a crossroads

In a world where customers expect instant answers, convenience, and reassurance, pharmacies can no longer operate as silent shelves and overworked counters. They must become orchestrators of care.

That shift doesn’t start with more apps or more staff. It starts with intelligence. Agentic AI is the layer that remembers, reasons, and responds in real time, shaping an experience that feels more like help and less like hassle.

Open this agent simulation in a new tab

From the ‘Order taker’ to the ‘Everyday health concierge’

The traditional pharmacy model responds to demand. But people don’t always walk in knowing what they need. The agentic leap involves flipping the model, from reacting to requests to anticipating health needs based on context, history, and symptoms.

The mantra: Pharmacy at the speed of symptoms.

Delivering intelligence to customers

Let’s look at the customer journeys where pharmacies can deliver "Wow" moments via agentic AI:

The first touchpoint (agentic triage)

Instead of a search bar, the user sees a prompt: "How are you feeling?"

Customer: "My toddler has a 102-degree fever and a red rash on his chest. What do I do?"

Agentic Action: The AI doesn't just list Ibuprofen. It reasons through pediatric safety guidelines, checks the user’s past purchases to ensure no allergy conflicts, and says: "I’ve identified two OTC options. I’ve reserved the grape-flavoured liquid (which he liked last time) at your nearest store. It's ready for pickup. Should I also book a tele-consult with a doctor just in case?"

Visual intelligence (computer vision)

The pharmacy app becomes a diagnostic tool.

Scenario: A customer isn't sure if a skin patch is just dry skin or an infection.

Agentic Action: The user snaps a photo. The Visual AI analyses the texture and colour. It doesn't give a definitive diagnosis (for liability), but it suggests: "This looks like a localised allergic reaction. I’ve highlighted an antihistamine cream currently on Shelf 3. I’ve sent a 'Pathfinder' map to your phone to help you find it the moment you walk in."

When the store recognises you before you walk in

The "Wow" factor peaks when the digital agent interacts with the physical store.

Geofencing Action: As the customer’s phone enters a 500-meter radius of the store, the Agentic AI pings the pharmacist’s tablet: "Customer Rahul is 2 mins away for 'Relief Kit #402'. Prepare for curbside pickup."

Result: The customer pulls up, a staff member hands them the bag, and the AI says: "Payment processed via your vault. Your dosage schedule is now set in your calendar."

When your medicines refill themselves

Three days before depletion: "Your BP medicine is due. Want me to hold the same brand at your regular location?"

When the follow-up feels like care, not spam

Two days after purchase: "You should be halfway through your antibiotic. Any side effects? I can suggest a probiotic that’s in stock."

Technical architecture of the "Smart chemist"

To achieve this, the pharmacy needs more than a chatbot; it needs a Reasoning Stack.

The Brain (Clinical Reasoning): A model trained on pharmacological datasets to understand drug interactions and symptoms. Fine-tuned on pharmacological data for safe, relevant, age-appropriate guidance.
The Memory (Health Context): A "Long-term Health Profile" that remembers Dad is allergic to sulfa and the family prefers sugar-free syrups. Health profiles, allergies, refill cycles, taste preferences.
The Hands (APIs): Hooks into inventory, loyalty engine, store location services, and payment gateways for frictionless checkout.
The Face (Generative UI): Instead of plain text, the AI generates an "Action Card" with a map, a "Buy Now" button, and a video tutorial on how to administer a nebuliser. Telling customers not only what to take, but also how to take it, and how soon they can get it.

When intelligence meets everyday health

Agentic AI doesn’t replace your pharmacy team; it empowers them. It brings intelligence to every touchpoint: before the visit, during the interaction, and after the transaction.

This is how local pharmacies stay relevant, not by racing to match ecommerce on price, but by building trust, context, and care into every visit. It’s the difference between handing over a bill and helping someone feel better.

Curious how agentic AI can work inside your pharmacy chain? Talk to an expert at DronaHQ to explore.

Building a RAG based agent using DronaHQ

Gayatri Sachdeva — Mon, 22 Dec 2025 12:37:00 +0000

RAG and agentic RAG are often discussed at a high level, which makes them sound either overly academic or unrealistically autonomous. In practice, most useful systems sit somewhere in between. This post breaks the topic down clearly. What RAG actually is, what agentic RAG adds on top, what agentic RAG looks like in real business operations, and how I built a RAG-based agent for internal ops without writing any code.

This RAG agent is based on an actual implementation by me for our marketing-sales enablement function. If you'd like to exchange ideas, lets connect!

What is retrieval augmented generation (RAG)

Retrieval augmented generation is a pattern where a language model generates responses using external data retrieved at runtime, instead of relying only on its training knowledge.

At a minimum, a RAG system has three parts:

A corpus of source material that represents ground truth.
A retriever that selects relevant pieces of that corpus based on a user query.
And a generator that produces an answer using only the retrieved context.

The key property of RAG is grounding. The model is constrained by what it retrieves. If the information does not exist in the corpus, the system should either say it does not know or ask for clarification.

In business settings, RAG is most valuable when accuracy matters more than creativity. Internal documentation, customer stories, policies, analytics, and transcripts are all natural fits because they are bounded and auditable.

What agentic RAG adds on top of RAG

Agentic RAG builds on the same retrieval foundation, but adds decision-making and iteration.

Instead of a single retrieve and generate step, an agentic RAG system can plan its work, adapt retrieval based on intermediate results, verify outputs, and take actions across tools. Retrieval still grounds the system, but the agent decides how and when retrieval happens.

Important distinction. Agentic RAG does not require full autonomy or open ended reasoning. In most enterprise use cases, agentic behavior is narrow, intentional, and bounded. Planning, verification, and clarification loops matter more than long reasoning chains.

A useful way to think about it is this. RAG answers questions. Agentic RAG completes tasks.

What agentic RAG realistically looks like in business operations

In practice (so far), agentic RAG systems do not behave like general assistants. They behave like focused workers with a clear job.

Think about a vendor renewal in a finance or operations team.

The information needed to make a decision lives in many places. Contracts in shared drives. Usage data in dashboards. Email threads where exceptions were discussed.

A basic RAG system can answer a question like “What does the contract say about renewal?” by retrieving and summarizing a clause.

An agentic RAG system does more. When asked to prepare a renewal summary, it first figures out what evidence is required. It retrieves renewal terms from the contract, pulls recent usage metrics, and searches for past exception notes. If any piece is missing or contradictory, it flags that instead of guessing.

Only then does it generate a structured summary that can be reviewed or shared. The key difference is not the output format. It is the planning, targeted retrieval, and verification before writing.

That pattern is what agentic RAG looks like in real business operations.

Building a RAG-based agent for business ops - Real life use case

Problem statement

Customer stories were valuable, but hard to use.

Information about a single customer lived across many places. A published blog might capture the high level narrative. Internal documents added implementation detail. Transcripts from customer story videos contained the strongest proof points and quotes. Slides and notes added yet another layer.

Any time someone needed a short customer bite, a few bullets for a deck, or a quote focused on a specific theme like integrations, the work was manual. Someone had to search across sources, reconcile overlaps, decide what was current, and rewrite everything for the new context.

The cost was not just time. It was inconsistency, outdated facts, and repeated rework.

Alternatives considered and why they failed

The first and default option was manual curation. Ad hoc requests to create custom one pagers, a bite for an email, or a slide for a deck.

ChatGPT and NotebookLM produced fluent answers, but accuracy was unreliable. They mixed customers, invented quotes, and blurred timelines. For customer stories, that risk was unacceptable.

Each alternative either did not scale or compromised trust.

The solution built

The solution was a RAG based agent scoped specifically to customer stories.

Instead of trying to know everything, the agent retrieves only verified, customer specific material at runtime and generates outputs strictly from that context. It does not rely on general model knowledge for facts or quotes.

Over time, this evolved into an agentic RAG system by adding planning, verification, and multi step behavior.

How the solution works conceptually

At a high level, the agent treats customer stories as evidence, not prompts.

When a request comes in, it first identifies which customer the request refers to and what outputs are required.

It then decides which sources are appropriate for each output. Narrative summaries come from blogs or internal docs. Quotes come from transcripts. Metrics come from outcome summaries.

The agent retrieves these pieces separately, assembles them into a working context, and only then generates the final output.

Nothing is written before retrieval. Nothing is generated without grounding.

How I built a RAG-based agent without coding

I used DronaHQ’s Agentic Platform to build this agent.

Resources: The first step was defining what the agent is allowed to know. I added only approved, existing resources that already reflect how we work. This included documents, transcripts, long form pages, and links. These sources live in different formats, but together they represent the full context the agent needs. Tip: Do not dump all resources into the system. Multiple sources of truth can confuse the agent. The goal is to give it the right data and nothing else.
Instructions: Instructions were the most important part of the build. I wrote instructions that explained what the agent is responsible for, how it should interpret vague requests, and what it must never do. I was explicit about avoiding overlap between sources and about asking follow up questions when the request is underspecified. Most of the iteration happened here. Small changes in instruction quality had a much bigger impact than changing tools or models.
LLM model: I selected the GPT 5 model and tuned it for lower creativity. The agent’s job is not to be clever. It is to be accurate and consistent. In this case, model choice mattered less than expected. Once the constraints and instructions were solid, the behavior became predictable regardless of model tweaks.
AI tools: I connected only the tools needed to produce outputs in formats people already use. This included document generation and Google Slides so the agent’s responses could immediately fit into existing tasks.
Testing: Testing happened continuously. I used the playground within DronaHQ to run real, messy prompts. I deliberately tested vague requests, edge cases, and scenarios where multiple sources could apply. Whenever the output drifted or overlapped, I refined the instructions instead of adding complexity.

This tight feedback loop made it easy to improve behavior without rewriting anything.

Steps involved in a real request

Prompt:

“Give me a 100 to 150 word bite on customer XYZ, add three to four bullet points, and include two quote options where the customer talks about integrations.”

The agent limits its search to artifacts tagged to customer XYZ.
It identifies three output sections and maps each to a source type. Blogs or docs for the bite and bullets. Transcripts for quotes about integrations.
It pulls the most relevant story sections and transcript segments that mention integrations.
Retrieved content is grouped by purpose so summaries and quotes do not bleed into each other.
The agent writes the bite and bullets using only retrieved story material and generates quote options derived from transcript language.

If the retrieved material is insufficient or contradictory, the agent does not guess. It either asks for clarification or limits the output.

This sequence is what turns a scattered knowledge base into reliable, reusable customer storytelling.

Mapping RAG concepts to the agent accurately

The corpus is the full set of customer story artifacts. Blogs, internal write ups, outcome summaries, transcripts, and tagged notes form the grounding data. The agent is not allowed to answer beyond this material.

Ingestion brings these artifacts into the system in a retrievable form. Stories are split into logical sections such as problem, solution, integrations, scale, and outcomes. Transcripts are chunked into conversational segments.

Retrieval happens at runtime. When a user mentions a specific customer, the agent scopes retrieval to that customer only. When the user asks for integration related quotes, the retriever pulls transcript segments that semantically match integration discussions.

Context assembly separates sources by purpose. Blog and document content is used to generate narrative bites and bullets. Transcript content is used to generate quote options.

Generation is constrained. The model is instructed to rely only on retrieved context. If the context is insufficient, the agent asks a clarifying question or declines to invent.

Where agentic behavior comes in

The first agentic layer is planning. Before retrieving, the agent decomposes the task into outputs and evidence needs. This determines which sources to query and how deeply.

The second layer is multi pass retrieval. The agent retrieves broadly first, then runs targeted retrieval for gaps such as integrations, metrics, or quotes.

The third layer is verification. The agent builds an internal evidence table mapping each claim to a source. Conflicts across sources are flagged or resolved using predefined rules.

Quote handling is agentic as well. The agent first extracts verbatim transcript lines related to the requested theme, then selects the strongest candidates. Cleaned versions are generated only after selection.

Slides introduce additional decision making. The agent chooses a narrative structure, retrieves proof points per slide, generates titles and speaker notes, and adapts the deck to the intended audience.

A final quality check loop evaluates grounding, completeness, and format compliance. If the output falls short, the agent revises using additional retrieval.

Closing thoughts

If you are exploring RAG, start with a workflow where the cost of being wrong is obvious. Customer stories, policies, support runbooks, vendor renewals, and internal SOPs are all good candidates because the source material already exists and the outputs are used by real teams.

A basic RAG system will get you faster answers. The moment you need consistent outputs that hold up under review, you will start adding agentic layers. Planning so the system knows what to fetch. Multi pass retrieval so it can fill gaps. Verification so it can flag contradictions. Clarifying questions so it stops guessing. Those upgrades are what move the system from “helpful” to dependable.

In my case, the shift was practical. The goal was not an autonomous agent. The goal was to stop redoing the same customer story work every week, and to make sure the outputs stayed grounded in what the customer actually said and what we actually shipped.

If you are building something similar, the biggest unlock is a combination of your stack, how you structure your resources, and the guardrails you add for the agent to overcome what other AI chatbots could not.

If you want to see the agent in action, the video above shows a real request end-to-end.

If you are building your own RAG or agentic RAG workflow, I would love to hear what corpus you are grounding it on and where it breaks today.

Trying out top AI app builders | Review and first impressions

Gayatri Sachdeva — Mon, 01 Dec 2025 07:30:00 +0000

Note: This is a rapidly evolving space with lots of amazing tools that fit a variety of builders and use cases. Please take the feedback below with a grain of salt. Reviews are our own. For latest updates, check platform sites. The list is evolving so Bookmark to come back for more reviews. Thank you and enjoy the read! :)

I’ve spent the past many months deep in the trenches with AI app builders, watching this space explode in 2025. I’ve poked and prodded at dozens of tools that promise to turn natural language into working software. Some delivered delightful surprises; others, not so much. In this article, I’ll share my hands-on impressions of the top AI app building platforms, organized by category. We’ll cover everything from AI code generators to form builders to autonomous agents. I’ll be frank about what it was like to build with each, where they shine, where they stumble, and how they fit into a real development workflow (beyond the hype). If a tool isn’t fully reviewed yet, I’ll note it as “review coming soon” as I plan to keep this updated as these platforms evolve.

Let’s dive in and see which of these AI co-builders have earned a place in a developer’s toolkit (and which are on thin ice).

AI code generators

AI pair programmers that generate actual code from natural language. These tools aim to speed up coding by producing code snippets or even entire project scaffolds based on prompts. They range from “chat with an AI to build an app” services to IDE extensions that autocomplete your code. After testing these, I’ve found they’re fantastic for jumpstarting projects and boilerplate, though each has its quirks and limits.

1. DronaHQ AI

What it is: DronaHQ is a developer platform (think dashboards, admin panels, CRUD apps) that added a robust AI Assistant to its low-code app builder this year. Essentially, it combines their mature visual development environment with generative AI to allow users to build apps by describing them in natural language or uploading a design. It gives you the speed of AI-generated apps with the structure and maintainability.

Building with it: I had an existing database of customer orders and wanted to quickly build an admin panel to view and search orders. Normally, in DronaHQ, you would drag out a UI component, say a table grid, connect it to the database, configure queries, etc.

With DronaHQ AI, you instead open their AI chatbot interface inside the builder and type: “Build an order management dashboard: list of orders with filter by date and status, and a form to update an order’s status with @ Database”

The AI goes to work, and within about 30 seconds, it generates a BRD that you scan through and approve or reject. It then generated two screens: one had a table component bound to my Orders table, complete with filter controls for date and status. The second screen had a form with fields for the order details and a dropdown for status, and even a “Save” button that was wired to an update query. It is a pixel-perfect and functional starting app.

It also used DronaHQ’s standard components and best practices: e.g., it used their pre-built table component and form controls, not some custom HTML.

After a generation, I switched to the visual editor (this is not exactly switching, as the AI chatbot is baked into the platform's visual editor console). All the components were there, and I could manually tweak them (change labels, reposition things) with drag-and-drop. The AI had pre-bound the data: the table’s data source was set to my Orders DB query, and the form’s fields were mapped to a selected row from the table. I did have to refine a bit: the filter by date was initially just a text field, so I replaced it with a date picker component. Interestingly, these tweaks are also AI-powered. I could just tag the UI component and ask AI to update it. I told the AI chatbot, “Make the date filter use a date picker,” and it actually did adjust it to a date picker control and updated the query binding to filter correctly. That felt like pair-programming inside a UI builder.

One more test: Vision AI feature, where you upload a design file (I exported a simple form screen from Figma). DronaHQ AI analysed the image and generated a UI layout in the app matching it, using their standard controls. It got the layout about 90% correct. I had to add an image carousel, but it was still a time-saver versus building from scratch.

Advantages:

Maintains structure: This is DronaHQ AI’s big selling point. The output isn’t some black-box code or one-off HTML; it’s composed of DronaHQ’s vetted components and connectors. That means after AI generation, your app is maintainable. I can continue to use the normal DronaHQ builder on it. When I generate an app, it follows a design system, uses proper data bindings, etc. In my use, I noticed the generated UI had consistent styling and spacing using our company’s theme that I’d configured. It felt production-quality, not just a throwaway prototype.
Two modes of building, prompt or visual, are interchangeable: Seamlessly go from AI chat to manual editing and back. For example, I generated a screen by prompt, then I manually added a chart component, then I asked the AI to “bind this chart to show total orders by day”; it understood my existing component and wrote the query for it. That synergy was great. It’s not AI-only; it’s AI plus visual drag and drop together.
Data integration out of the box: DronaHQ connects to tons of databases and APIs. The AI leverages that, when I start a prompt, I can tell it which data source to use. Once I picked my Postgres DB, the AI could actually see the schema (it asked me to confirm table names in its BRD). It then formed queries under the hood. It basically eliminated the configuration steps by inferring what I wanted. Since it’s built on the visual dev platform, it automatically handled details like auth, deployment, and user roles from the platform’s settings, stuff that code-gen tools would ignore.
Pricing and self-hosting flexibility: They have a pricing model for AI credits (e.g. $30/month for 2000 AI credits. I could also connect my own OpenAI API key and use that, which is nice.

Limitations:

Domain knowledge is limited to your prompt clarity: The AI doesn’t magically know your business rules unless you express them. In my order dashboard example, I had to specifically prompt for the features I needed. If I was too vague (“make an order dashboard”), initially it made some assumptions that weren’t quite right (it included a “Create Order” form which my use case didn’t need). I had to iterate with the prompt. For a more complex domain (say a compliance audit tool with specific logic), I suspect I’d still spend time refining the AI output. It accelerates the build, but you must guide it well.
Occasional layout quirks: The generated UIs are good, but sometimes the layout needed tweaking. One time it chose a slightly odd component (e.g., it used two dropdowns for a range filter where maybe a single slider would have been better). Visual platform means I could fix it with drag-and-drop easily.
Focus on internal tools use cases: DronaHQ Veda AI is fantastic for CRUD apps, admin panels, forms, etc., basically enterprise internal tools. It’s not trying to be a consumer app builder like Netflix, Amazon, Airbnb of the world.

Where it fits: DronaHQ really shines in quickly standing up internal business applications. In the app lifecycle, it’s perfect for the initial development of tools that might otherwise take a backend dev + frontend dev several days to set up. With DronaHQ AI I got a functional app in minutes that I could then hand over to a team or end-users for feedback. It’s also useful for routine internal apps, those things like admin panels that every company needs. Instead of spending valuable dev time, an ops person could almost build it themselves with AI assistance. I also see it being used for iteration: if an app needs a new module or report, I can generate it quickly and plug it in. Importantly, DronaHQ ensures the AI-generated stuff isn’t throwaway; it’s a living app I can maintain. So I’d use it not just for a demo, but as the foundation of the real tool (with some verification before production, of course). Among all the AI app builders I’ve tried, DronaHQ AI felt the most grounded in practical usage, it wasn’t flashy sci-fi, it was like “here’s your app, it’s built following the same rules your devs would follow.” That goes a long way in making me trust it for serious use.

2. Lovable

What it is: Lovable (aka LOVABLE AI) is a prompt-driven app builder that turns plain English descriptions into full-stack web applications. Think of it as an AI software engineer that you chat with about your app idea. It then generates the React frontend, a Supabase backend, and even wires up auth and database if you ask. The acronym LOVABLE stands for “Letting Ordinary Visionaries Achieve Breakthroughs with Language-based Engineering,” which reflects its mission of making app creation accessible. (

Building with it: My first time using Lovable was a positive experience. I described a simple “to-do list app with user sign-up and a task dashboard,” and it spawned a React/Tailwind UI and a Supabase PostgreSQL schema for tasks. The speed really is insane for prototyping. Iterating was conversational: “Now add a filter to only show my tasks” resulted in an updated UI with a filter dropdown. Under the hood, it creates code (React + Vite project) and pushes it to a GitHub repo, so I could pull the code and tweak it manually when needed. This two-way workflow (AI generation plus manual editing) is a big plus. It shines for quickly scaffolding an MVP or admin panel.

3. Bolt

What it is: Bolt (accessible at bolt.new) is another AI-powered development environment for generating full-stack apps. It’s often seen as a direct alternative to Lovable. The key difference: Bolt runs entirely in your browser and is designed to be fast. The interface splits into a chat-like prompt panel on the left and a live code preview on the right (ref: thepromptwarrior.com). Instead of manually coding, you describe features and Bolt writes the code in real-time, showing you the resulting app instantly. It supports choosing a starting framework (like a Next.js blank project, etc.) and can do one-click deploys to the web.

Building with it: Using Bolt felt like having a super-charged ChatGPT. I followed a simple workflow the first time:

(1) Selected “Start with blank Next.js app” from Bolt’s menu, (2) Typed a prompt “Create a note-taking app with a title field and markdown support,” and (3) watched Bolt generate the code and spin up the app preview. In literally 2 minutes I had a running web app (and yes, I could type notes in it!). Hitting the Deploy button pushed it live via a Netlify integration, no configuration needed. The whole experience is like ChatGPT combined with a lightweight IDE and a deployment pipeline.

Editing code in Bolt is technically possible (you can click into the code it generated), but the UI nudges you to prompt rather than hand-edit. It feels more like a no-code tool in that sense, I found myself iterating by telling the AI what to change (“Make the notes sortable by date” etc.) instead of typing code. That’s great for non-programmers or quick tries, though as a programmer I occasionally wanted to fine-tune the code directly and the editor wasn’t super friendly for that. Bolt is clearly optimized for “type instructions, not code.”

Advantages:

Fast scaffolding & deployment: As mentioned, Bolt is the quickest way I’ve seen to go from zero to a live web app. It automates all the setup (choose your framework, then it handles dependencies, file structure, etc.) and hosting. With Bolt’s built-in deployment, I had a shareable URL of my app with literally one click. In contrast, with some IDE-based tools I’d have to copy code to Vercel or similar to deploy. This is a huge win for hackathons or internal demos.
Beginner-friendly UI: The Bolt interface feels like a familiar chat or like using ChatGPT, which is less intimidating than opening a full IDE. A friend with minimal coding experience tried it and loved that she didn’t have to “see code” unless she wanted she could just describe features and watch them appear. It lowers the entry barrier for making small apps or prototypes.
Great for UI scaffolding: Bolt excelled at creating the basic UI and connecting common services. It had templates for things like simple CRUD apps, and it could integrate things like a Stripe payment button or a Google Maps component if you prompt correctly. It felt like it had a toolbox of components and recipes to draw from, making it adept at standard web app patterns.

Limitations:

Not suited for complex apps: When I pushed Bolt beyond simple to-do-list complexity, it started to flounder. In one test, I attempted a more complex inventory management app with user roles, batch operations, etc. Bolt began making mistakes, it would produce code with errors, then try to fix them in a loop, sometimes regressing or getting confused. This aligns with what others have noted: Bolt is awesome for quick scaffolds, but “good luck building something more complex with it!”, it tends to run into errors and self-fix loops on larger projects. In my case, after a certain point I had to stop and export the code to a regular editor to finish the job.
Limited code editability in-app: The Bolt editor is minimal. If Bolt’s AI writes something slightly off, there is not a rich editing or debugging environment to step in and tweak. The expected use is you keep prompting the AI to change the code. While that’s fine for high-level changes, it was frustrating for small fixes, e.g. I wanted to adjust a single CSS rule, and doing that via a prompt (“make the header blue”) sometimes caused the AI to regenerate more than I wanted. Essentially, control is limited. As an advanced user, I felt constrained when I needed a specific edit. The workaround is to export the project to Stackblitz’s full editor or download it, but that breaks the flow.
No backend beyond serverless functions: Bolt can set up a Next.js or Express backend for simple API routes, but it doesn’t provide database integration by default (unlike Lovable with Supabase). You’d have to manually connect a database or use an API. So it’s more front-end heavy. For persistent data beyond session/demo, you must incorporate external services.
Pricing model: Currently Bolt is free (in beta), but it’s unclear how they’ll charge long-term. If they impose usage limits or a subscription, one will have to consider cost vs. using a free open-source template + Copilot.

Where it fits: Bolt is my favorite for throwaway prototypes and quick demos. When I need to prove an idea in minutes or create a small utility app, I reach for Bolt. It’s like a super rapid WYSIWYG for web apps. I also sometimes use Bolt to kickstart a project’s skeleton, then immediately export to a dev environment for serious development. For example, I’ll have Bolt lay out a basic Next.js project with a couple pages (to save me the boring setup), then I’ll take over in VS Code + Cursor for the heavy lifting. Bolt is less useful for large, production-bound projects, except as a starting boost. I see it as a complement to more robust tools: use Bolt to get the basic skeleton (fast UI and deployment), then switch to a full IDE or another AI coding assistant for the deeper logic and maintenance.

4. GitHub Copilot

What it is: GitHub Copilot needs little introduction at this point, it’s the AI coding assistant integrated into VS Code (and other IDEs). I’ve been using Copilot since its early days, and by 2025 it has evolved significantly. It acts like an AI pair programmer: it autocompletes lines or whole functions as you code, and with the newer “Copilot Chat” and Copilot CLI/Agent modes, it can also follow natural language instructions (inside your editor) to refactor code or even run multi-step dev tasks. Essentially, Copilot is baked into your coding workflow, rather than replacing it.

Building with it: My experience with Copilot is different from the likes of Lovable or Bolt, Copilot doesn’t generate an entire app from a blank prompt. Instead, it’s there with me as I write code. For example, I start writing a function in Python to parse some data, and Copilot suggests the rest of the logic almost like an autocomplete. It often correctly infers what I’m trying to do (from the function name or comment) and gives me a draft of the code. With Copilot Chat (released as part of “Copilot X” upgrades), I can highlight a block and ask, “Hey, optimize this loop” or “Find the bug here,” and it responds in the sidebar with code changes or explanations. By mid-2025, Copilot even introduced an “agent mode” in VS Code that can take higher-level objectives (e.g. “Create a new component and unit test for feature X”) and attempt to execute them, somewhat like Cursor’s agent did. Microsoft has integrated these features deeply, I’ve seen Copilot suggest pipeline configurations, documentation, even SQL queries based on my database schema. The scope of what Copilot covers in development keeps growing.

Advantages:

Embedded in my workflow: Copilot lives in VS Code while I code, which means I don’t have to switch contexts. This is huge for productivity. Compared to using an external chat tool (like ChatGPT web or Lovable) and copy-pasting code, Copilot feels seamless.
Highly consistent for code completion: Over time, I’ve noticed Copilot’s suggestions have gotten more reliable. It’s trained on tons of public code, so for standard tasks it’s eerily good. Writing boilerplate or repetitive code is now something I rarely do manually, Copilot fills in the blanks 80% of the time with minimal edits needed. Users have found it consistently solid: one review noted that Copilot (especially with GPT-3.5/4 models) provides steady, dependable completions day-to-day. I agree, it might not always hit bulls-eye on the first try, but it rarely spits out something completely off-base.
Reduced cognitive load: I can focus on what I want to achieve, and Copilot figures out the how in code syntax. For instance, I can write a comment “// sort list of events by date desc” in JavaScript, and Copilot will write the sort function. It’s not just saving keystrokes; it’s saving me from having to recall exact API names or language quirks. It’s like having Google/StackOverflow knowledge contextually as I code.
Now with multi-step “agent” capabilities: A big development in 2025 is that Copilot isn’t limited to one-file suggestions. GitHub (and Microsoft) rolled out a preview where Copilot can perform multi-file refactors or create new files on command (in VS Code Insiders). I played with this “agent mode”, for example, I typed a natural language command to “Add a new route and controller for user profiles” in a Node.js app. Copilot proceeded to generate a new file, modify the routing config, and suggest relevant code in each place. It’s not always perfect, but it felt like having a junior dev who takes a first pass at a multi-file change. This closed much of the gap that existed between Copilot and a more autonomous tool like Cursor or Cody. In fact, by this point Copilot and Cursor have converged in capabilities, both have chat and agentic features for coding tasks.

Limitations:

IDE-specific and not a full app generator: Copilot is fantastic when I’m in coding mode, but it won’t design your app from scratch via conversation. It’s not a replacement for something like Lovable if you have zero code and just an idea, instead, you need to have a project and start writing code, then Copilot assists. So non-developers might find it less immediately gratifying. It’s really a developer’s tool.
Quality depends on context and codebase: In a new or poorly documented codebase, Copilot has less to go on. I noticed that when I work on proprietary or very unique code, Copilot’s suggestions are hit-or-miss because it doesn’t “know” the context beyond what I’ve written. It shines with familiar patterns and frameworks. Also, earlier in 2025 Copilot’s context window was expanded (especially if using GPT-4 model under the hood), which helped it consider more of my code at once. However, it still sometimes loses the thread on very large files or projects.
Costs and limits: GitHub Copilot is a paid subscription ($10/month for individuals), which is reasonable given how much time it saves me. They did implement some usage limits: I read that as of May 2025, they introduced a cap of ~300 requests per month on the highest tier model, presumably to control costs. In normal use I personally haven’t hit a hard limit, but extremely heavy users might. Meanwhile, competing IDE assistants like Cursor charge more (Cursor is about $20/mo and even then caps “fast” requests), so Copilot still feels like good value.
Still makes mistakes: It must be said, Copilot can and does generate errors. It might use an outdated API or a wrong variable name occasionally. I’ve had instances where it confidently wrote a function with a subtle bug. So you cannot blindly trust it. It requires the developer’s oversight and testing. Think of it as a helper that writes 80% correct code, and you are responsible for the remaining 20% fixes and validation. It won’t magically handle complex new logic you don’t understand; it usually needs guidance or corrections from you for those scenarios.

Where it fits: GitHub Copilot is a must-have in the coding stage of app development for me. It’s not about prototyping a whole app (use other tools for that); it’s about day-to-day coding productivity. When I’m writing actual production code, Copilot is my constant companion. It speeds up implementing features, writing tests, generating boilerplate (like DTOs, config files, etc.), and even helps in learning new frameworks by example. It’s most beneficial to developers who are actively coding, it won’t replace understanding system design or requirements, but once I know what I need to build, Copilot makes the act of writing the code much faster and less tedious.

5. Cursor IDE

What it is: Cursor is an AI-powered code editor, basically an entire IDE built around an AI assistant. Imagine VS Code, but with a chat sidebar that has deep knowledge of your project, and the ability to make project-wide changes via prompts. That’s Cursor. It gained popularity in late 2024 as one of the first IDEs to tightly integrate an AI agent (Claude by Anthropic, in fact) into the coding workflow. By 2025, Cursor has become a strong competitor to Copilot, especially for those who wanted more autonomy from the AI and a dedicated environment for AI-assisted coding. I’ve been using Cursor for some of my projects to compare it with Copilot.

(Review coming soon)

6. Claude 3.5 (Anthropic “Sonnet”)

What it is: Claude 3.5 (nicknamed “Claude-Sonnet” for the version tuned for coding) is Anthropic’s latest large language model, which many tools (like Cursor) use as their AI brain. While not a product with a user interface by itself, I’ve interacted with Claude 3.5 via other apps and via API. It’s known for huge context windows and a cooperative, less deterministic style. Some devs use Claude in Slack or other chat interfaces as a coding aide, similar to how one would use ChatGPT.

(Review coming soon)

7. DeepSeek V3

What it is: DeepSeek V3 is the dark horse in the AI world that emerged out of China and caught everyone’s attention in 2025. It’s a massive 600+ billion parameter Mixture-of-Experts model (with about 37B active per token), and it’s available through an app and open-source weights. DeepSeek made headlines for reportedly outperforming models like Meta’s Llama and even challenging GPT-4 on certain benchmarks. It went viral when their mobile chatbot app topped app store charts globally. So naturally, I was intrigued to see how DeepSeek handles coding tasks and app building.

(Review coming soon)

8. ChatGPT (GPT-4.5)

What it is: ChatGPT, the trusty AI chatbot that started it all, is still very much in my arsenal for coding and app building. By late 2024, OpenAI rolled out GPT-4.5 (an intermediate upgrade to GPT-4), and I’ve been using ChatGPT with GPT-4 (and presumably 4.5) throughout 2025. It’s worth covering here because ChatGPT effectively acts as an AI app builder when used well: you can converse with it to generate code, solve errors, and even use plugins or the Code Interpreter (renamed Advanced Data Analysis) to run code.

(Review coming soon)

Visual + AI app builders

AI copilots for low-code platforms. In this category, we have platforms that traditionally offer drag-and-drop app building (for web or mobile internal tools), now supercharged with AI to generate complete or parts of the app for you. These are great for those who don’t want to write raw code for everything. You define data models and UIs in a GUI, and the AI helps fill in the gaps (or even builds entire modules from a prompt). I spent time with several low-code builders that rolled out AI features in 2025. Overall, they accelerate the tedious parts of app assembly, though the experience can vary depending on how well the AI integrates with the platform’s components and what the vendors have sought to solve. Let’s go through the notable ones I’ve tried:

9. ToolJet AI

What it is: ToolJet is a popular open-source low-code platform for internal tools (similar to DronaHQ in concept). In 2025, ToolJet introduced AI features to their builder, often referring to an “AI agent builder” and AI-assisted app creation. Given ToolJet’s open-source nature, I was curious how their AI integration stacks up, especially since they hype “build apps with natural language in minutes” in their blog.

Building with it: I spun up ToolJet (hosted version) for a test project, a simple HR employee directory app. ToolJet’s AI appears as a sidebar where you can chat or give commands while in the builder. I typed something like: “Create an employee directory app with a table of employees (name, title, department) and a form to add a new employee.” The AI responded by creating a new application with those components. It added a table widget, pre-named “EmployeesTable,” and a form with text fields for name, title, department, plus a submit button. It also generated a little query logic: since I hadn’t connected a real data source, it created a sample data array inside a ToolJet query as placeholder data (which was clever, it meant I could see it working immediately and swap in my DB later). The new employee form was set to append its input to the table’s data on submission for demo purposes.

ToolJet’s AI also highlighted potential validation and debugging hints. In the chat it said, “I’ve created the app. Next, consider connecting this to a database or API. I can help with that too. Also, I enabled debug logs in case the form submission fails.”, It’s like it anticipated the next steps I might need. ToolJet’s marketing mentions an AI “debugger” and indeed, I saw that if an error occurred (I simulated a failure by adding a failing query), the AI could explain the error and suggest a fix. It essentially reads ToolJet’s error logs and provides a human-readable interpretation. This felt unique, most builders focus only on generation, but ToolJet put effort into AI-assisted debugging (their blog calls it “AI debugging” as a feature).

I later connected a Google Sheets as a data source (since that’s a common quick backend). I asked the AI to “Bind the employees table to a Google Sheet with columns Name, Title, Department”. It guided me through the OAuth setup (opening a window for me to connect the Google account) and then it generated the integration: a query that fetches rows from the sheet and populates the table, and another query that appends a new row when the form is submitted. This was pretty magical, normally, figuring out Google Sheets API and connecting in a tool takes a bit of manual reading; the AI just did it. Under the hood, it utilized ToolJet’s existing connector, but it wrote the query code (which uses ToolJet’s scripting) automatically.

Advantages:

Open-source and extensible: The fact that ToolJet is open-source meant I could even self-host it and keep the AI features internal. For organizations hesitant to send data to an AI service, ToolJet offers an approach where you can configure it to use your own model or API key. I used their cloud, but knowing this flexibility is great. Also, the community can improve the AI prompts and logic since it’s open, I saw some GitHub discussions on prompt tuning.
AI debugging and error handling: This is a standout. I deliberately messed up a component’s binding and got an error. The AI debugger popped up a message like, “The query to add a new employee failed because the ‘Department’ field was empty (which is not allowed). Try providing a default value or making the field required.” That context-aware help is exactly what non-expert builders need. It reduces frustration when something isn’t working, the AI essentially acts as tech support, analyzing the situation.
Robust customization after generation: ToolJet’s AI uses the existing powerful features of ToolJet. After generation, I could still dive into the JavaScript editor to tweak a query, or add custom CSS. It’s not limiting me, rather, it jumpstarts and I have full control to refine. This means I could do fancier stuff (like writing a small JS transformer for data) that the AI might not handle automatically. ToolJet supports injecting code, and the AI sometimes even generated code blocks inside the platform’s scripting environment for me.
Enterprise readiness (security, deployments): ToolJet being aimed at internal tools means it has role-based access, on-prem deploy, etc., similar to DronaHQ. The AI respects those, e.g., when I built the app, it was still contained in ToolJet’s usual auth framework. This is important if I’m to trust the app beyond just a demo. ToolJet also touts compliance (SOC2, GDPR) and the AI doesn’t change that equation since it works within the app builder’s framework.

Limitations:

Still evolving prompt understanding: In my usage, ToolJet’s AI sometimes needed very explicit instructions. If I was too general, it either asked follow-up questions or made incorrect assumptions. For example, I first said “make an employee directory” without specifying fields, it defaulted to Name, Email, Phone. When I meant Title and Department, I had to correct it. It’s not a big deal, but to leverage it best I had to be clear and maybe build in steps: generate basic app, then enhance it with additional prompts. The UX was slightly less fluid than DronaHQ’s for initial prompting, but it’s improving.
UI/design limitations: ToolJet’s design options are somewhat basic (this is true without AI as well). The AI will use the standard components, which look fine but not flashy. If you want pixel-perfect, you’ll still have to manually adjust styling or inject CSS. The AI doesn’t automatically apply a bespoke design system or fancy layout beyond what ToolJet offers. So the resulting app might look somewhat utilitarian (which is usually fine for internal tools).
Performance concerns on self-host: While the cloud version was snappy for me, I’ve heard from peers that if you self-host with the AI features, you need decent server resources (especially if running an AI model locally). ToolJet’s AI might call out to OpenAI or others, so you also need to manage those API keys/costs. It’s not a plug-and-play offline solution (nor would one expect it to be). But for small teams without DevOps, setting up the AI builder might be non-trivial; using their hosted is easier and that means cloud (with your data going through them).
Documentation and learning: The AI is great, but understanding what it did sometimes required looking at the queries it created. ToolJet is developer-friendly, but a pure citizen developer might be a bit lost when the AI inserts a piece of JavaScript in a query. For example, after binding to Google Sheets, I inspected the query code to ensure it was correct. Some understanding of ToolJet’s environment was helpful. They might need to improve how the AI’s actions are presented (maybe a summary of “I created X, Y, Z for you”).

Where it fits: ToolJet AI is excellent for rapid internal tool development, particularly for teams that appreciate open-source or need on-prem solutions. I see it being used by startups or SMEs who already favor ToolJet for cost reasons (free, self-hostable), the AI now lowers the skill barrier to build apps on it. It’s the kind of tool where a non-developer PM or ops person could build a decent app with a bit of AI help and maybe occasional developer input for advanced parts. The AI debugging feature also means it’s forgiving for beginners, it helps you fix issues as you go. In the dev lifecycle, ToolJet AI would cover initial development through to perhaps maintenance (imagine later saying “AI, add a column for Employee Start Date to all relevant parts”, it could do that). While I still lean on DronaHQ or Retool for enterprise scale (they have more enterprise features), ToolJet’s community-driven innovation with AI is compelling. I’ll keep an eye on how it progresses. For now, it’s a cost-effective way to get AI-assisted app building if you’re inclined to go the open-source route.

10. Superblocks (Clark)

What it is: Superblocks is another platform in the internal tools/automation space, and Clark is what they’re calling their new AI agent. Clark’s claim to fame is being “the first AI agent to build internal enterprise apps”. The name comes up a lot in both Low-Code Builders and Autonomous Agents, because Clark spans both: it can build apps via AI and function as an AI ops agent. I got early access to Clark as part of a trial Superblocks offered after their big funding announcement.

(Detailed review coming soon)

Next, I’ll explore “Vibe Coding Tools,” which focuses on UI/UX design with AI, essentially, how AI assists in creating the look and feel (“vibe”) of an app. Bookmark this blog to visit again!

Build your first AI agent | Top 9 low-code agent builders

Gayatri Sachdeva — Tue, 18 Nov 2025 14:02:43 +0000

By now, we've all used LLMs like GPT-4 or Claude to accelerate coding, write documentation, or explain complex concepts. But those are still one-off prompts. You ask, it answers. That’s it.

AI agents go a step further. They can take actions, follow logic, call APIs, and complete tasks end-to-end. Instead of just helping you think, they can do the work beyond the chatbox.

We have talked at length about what an AI agent is. In this post, we’re examining tools that simplify the process of building these agents, eliminating the need to stitch together scripts or manage infrastructure from scratch. In this post, I’ve rounded up the current most useful low-code AI agent builders for teams who want to build, test, and run reliable AI agents fast.

What is an AI agent

AI agents are smart software programs that can work on their own or with minimal help. They can look at information, make plans, and carry out tasks by themselves, all while following the rules set by a business. These agents are part of a new wave of AI tools built to act with purpose, achieve specific goals, and adjust their approach as they learn. Over time, they get better at what they do, helping teams work more efficiently and produce higher-quality results.

With the ability to learn and improve over time, AI agents help modernise outdated systems and drive growth, making them a powerful tool for both small and large organisations aiming to stay competitive.

Real-life AI agent use cases

It is much easier to understand AI agents and also differentiate them from standard automations with the following examples:

Customer Support Automation: AI agents can handle customer inquiries end-to-end, resolve common issues, and escalate complex cases to human agents, reducing wait times and support costs.
Real-Time Fraud Detection: In banking, AI agents monitor transactions, flag suspicious activity instantly, and initiate investigations with minimal human input, enhancing security and compliance.
Healthcare Workflow Management: Agents can manage patient appointments, documentation, and ensure adherence to medical protocols, freeing up staff for critical care tasks.
Autonomous Financial Trading: AI agents analyse market trends in real-time and execute trades based on pre-set strategies, improving speed and decision-making accuracy.
AI Developer Assistants: Agents like GitHub Copilot help developers by generating, testing, and debugging code, accelerating software development cycles.

Did You Know?

In April 2025, a survey found that 96% of enterprise IT leaders plan to expand their use of AI agents over the next 12 months, citing roles in development support, security, and performance optimisation. It shows where the future of productive, tool-powered workflows is headed. Source

AI agent Builder

An AI agent builder is a tool or platform that helps users create, train, and deploy AI agents. These AI agent builders often come with easy-to-use interfaces and ready-made templates, making it simpler to build AI-powered applications and start using them in your day-to-day operations.

What is a Low-code AI agent builder?

A low-code AI agent builder differs from traditional AI agent builders. These tools minimize the need for traditional programming by offering more accessible ways to define how an agent works. This could be through visual interfaces like drag-and-drop builders, flowchart-style editors, or even simple prompt-based UIs where you describe what the agent should do in natural language.

What sets low-code platforms apart is their heavy use of pre-built, reusable components, like triggers, actions, and integrations, that users can configure instead of creating from scratch. This makes them ideal for those who don’t have the time, resources, or deep technical skills required to build agent systems the traditional way.

Depending on the platform, you might still be able to, or need to, write some code for advanced customizations, especially if it’s built with developers in mind. But regardless of how technical the end user is, the overall goal remains the same: to make it faster and easier to build powerful agent-based workflows that automate tasks, assist with decision-making, or even replace parts of human interaction.

In this blog, we will be looking at the current best Low-code AI agent builders.

1. DronaHQ low-code AI agent builder

DronaHQ includes an agent builder designed for enterprise operations where agents need to do real work, not just run as isolated chat experiments. It lets teams define an agent’s goal, tools, and context, and then choose how that agent is triggered or invoked. This removes the usual split where the logic lives in one tool and the interaction surface has to be built separately.

The platform supports a standard chat interface and multiple trigger surfaces, so you can have agents can run through chat, email, webhooks, schedules, or workflow actions. Developers can combine reasoning steps with tool use, data access, and code when needed, making it suited for tasks that mix intelligence with operational execution.

Watch the launch keynote

Key features

Intuitive no-code agent builder interface
Define intructions in simple natural language
Goal and tool-driven agent setup for operational tasks
Standard chat interface for agent interaction
Multiple triggers, including email, webhook, schedule, and in-workflow actions
Tool access for APIs, databases, and internal systems (via MCP servers)
Mixed logic combining agent reasoning with JavaScript, Python, and workflow steps
Optional knowledge bases for grounded reference

What it focuses on

Agents that resolve or streamline daily operational pain points
Use cases that blend reasoning with structured actions
Situations where teams want both the logic and the invocation layer in one place

Current applications

SQL agents for reporting and data lookups
Document understanding and verification flows
Internal support and ITSM assistants
Multi-step ops workflows that require reasoning plus execution

2. Botpress

Botpress is an open-source conversational AI platform that now includes Autonomous Nodes, blocks that turn chat flows into fully fledged AI agents. Teams define an agent’s goal and personality in plain language, then mix structured dialogs with LLM reasoning, 120-plus integrations, and usage-based cloud hosting to deploy support bots or workflow helpers fast.

Key features

Autonomous Nodes for Goal-Driven Agents
Visual drag-and-drop flow builder
100 + channel & API connectors
Open-source core with scalable cloud hosting

What It Misses:

Requires solid prompt engineering for stability
Autonomous behavior may unpredictably alter flows without clear logs
Potential instability during model upgrades

Current Applications:

Customer support bots with dynamic context switching
Lead‑gen assistants that integrate with CRMs
Internal support workflows with multi-channel deployment

3. Gumloop

Gumloop is a no-code AI automation framework that lets you design agentic workflows with ready-made components and custom nodes. Drag blocks for data extraction, scoring, or notifications, trigger flows via email, Slack, or webhooks, and monitor runs in a unified dashboard, ideal for operations teams that need hands-free, end-to-end automation.

Key features

Library of pre-built AI components
LLM-powered custom node designer
Hands-free triggers (email, Slack, webhooks)
Real-time monitoring & error handling

What It Misses:

Still evolving community ecosystem
May not support highly customized logic

Current Applications:

SDR & lead‑scoring automation
Document processing, SEO monitoring, and CRM updates.

4. Flowwise AI

FlowiseAI is an open-source visual builder for LLM workflows and autonomous agents. Its node-based canvas puts LangChain concepts, tools, memory, and retrieval into drag-and-drop blocks, so developers iterate quickly without heavy Python. Self-host with a single npm start or use the managed cloud for autoscaling endpoints, perfect for startups prototyping agent systems.

Key features

Node canvas for LangChain agents
Built-in RAG & vector-store modules
One-command self-host or managed cloud
Extensible via custom-node SDK

What It Misses:

Lacks enterprise governance & pre-built connectors
Requires LangChain familiarity for depth

Current Applications:

Rapid prototyping of retrieval‑augmented agents
Integrating custom tools/APIs into visual flows

5. n8n

n8n is an open-source workflow automation tool that now ships AI Agent nodes. Compose multi-step automation that calls LLMs, pulls data from 400 + integrations, and feeds results back into apps, all from the same low-code canvas. Agents inherit n8n’s visual logic, branching, and retry controls, making it easy to enrich customer support, marketing, or data pipelines.

Key features

AI Agent node with prompt & memory
400 + pre-built app connectors
Cloud or self-host deployment
Visual branching & error-handling logic

What It Misses:

UI is less polished for complex logic
Advanced nodes require JavaScript

Current Applications:

Automated customer outreach via AI
Enriched ticket systems and lead scoring
Internal data pipelines with intelligence

6. Microsoft Copilot Studio

Microsoft Copilot Studio is a low-code builder for autonomous agents across the Microsoft 365 stack. A graphical designer lets anyone define goals, add enterprise data, and publish agents into Teams, Outlook, or SharePoint. New “computer use” automation lets agents click and type in legacy apps without APIs, while analytics and compliance controls satisfy IT.

Key features

Drag-and-drop agent designer
Access to Microsoft Graph data & Actions
“Computer-use” UI automation for legacy apps
Usage-based pricing inside Microsoft 365

What It Misses:

Limited outside Microsoft stack
Licensing tied to 365 subscriptions

Current Applications:

Automated scheduling and email summarization
Legacy app automation without APIs
Secure digital assistants for enterprise tasks

7. Zapier Agents

Zapier Agents add autonomous decision-making to Zapier’s no-code automation. Describe an outcome (e.g., “triage new support email”) and the agent chooses among 8,000 + Zapier actions, learns from feedback, and documents its steps. A generous free tier and pay-as-you-grow plans make it easy to drop agents into existing Zaps.

Key features

Natural-language agent builder
Access to 8,000 + app integrations
Feedback loop for continuous learning
Works alongside classic Zaps

What It Misses:

Decision-making is limited to Zap workflows
Some logic still requires manual adjustment

Current Applications:

Auto-triaging support email via Agent + Zap
Assisted workflow creation without scripting

8. Appsmith

Appsmith is an open-source low-code platform for internal tools that now embeds AI Agents. Drop an “Agent” widget into any dashboard, connect it to your databases or REST APIs, and let it resolve tickets, generate reports, or update CRM records. JavaScript logic, RBAC, and Git sync keep developers in control while non-technical users build visually.

Key features

Drag-and-drop UI builder with agent widget
Connect to DBs, REST, GraphQL, & LLMs
JavaScript scripting & Git versioning
RBAC and audit logs for compliance

What It Misses:

Decision-making is limited to Zap workflows
Some logic still requires manual adjustment

Current Applications:

Auto-triaging support email via Agent + Zap
Assisted workflow creation without scripting

9. Retool AI Agents

Retool is a popular internal tool builder that recently introduced Retool AI , a way to embed agentic logic into low-code workflows. With Retool AI Agents, you can design apps that ask questions, make decisions, and execute logic using LLMs. It’s deeply integrated into Retool’s existing environment, meaning your agents can read from and write to databases, call APIs, and interact with UI elements inside your apps, all with natural language.

It’s especially useful for teams that already use Retool for internal dashboards and want to supercharge them with smart, autonomous behavior.

Key features

Integrates GPT-based agents inside internal apps
Works with Retool’s drag-and-drop interface and custom JS logic
Agents can take action (write to DBs, call APIs, send emails)
Built-in feedback collection and refinement via “Agent Playground”

What It Misses:

Retool Agents are still relatively new and evolving
Requires a Retool subscription (enterprise features gated)
Limited to use within Retool apps (not a standalone orchestration layer)

Current Applications:

Smart internal dashboards that respond to queries and automate data entry
Autonomous CRM record updating and outreach
Agents that summarise support tickets or write SQL queries dynamically

Conclusion

AI agents are no longer just futuristic concepts, they’re working behind the scenes in real businesses, handling everything from customer support to fraud detection, data analysis, and automation. And with the rise of low-code builders like Botpress, Gumloop, FlowiseAI, n8n, Microsoft Copilot Studio, Zapier, Appsmith, and Retool AI Agents, it’s now easier than ever to bring these agents to life, without writing everything from scratch.

Whether you’re a developer exploring multi-agent workflows, an ops team looking to automate repetitive tasks, or just curious about what AI can do beyond chatbots, these platforms open up an exciting new frontier. Each tool comes with its own strengths, trade-offs, and learning curves, but they all share one goal: helping you move from ideas to execution, faster.

How to build a loan application using MySQL in DronaHQ

Gayatri Sachdeva — Fri, 17 Oct 2025 10:23:11 +0000

DronaHQ is an AI-powered dev platform that empowers teams to build internal tools, apps, and workflows with powerful backend logic and UI design, all without requiring any coding expertise. Whether you’re working with REST APIs or databases like PostgreSQL or MongoDB, DronaHQ makes it seamless to connect, fetch, and manipulate data visually.

In this loan processing application, we will build a comprehensive dashboard that displays data from a connected MySQL database. The dashboard will include a dynamic table to track the status of loan applications, whether they are accepted, rejected, or pending. Users will be able to view and update their loan status through a pre-filled form with existing data.

Additionally, we will implement a smart EMI calculator that computes the monthly instalment based on the loan amount and duration. The app will also include features to add new loan entries using a multi-tabbed form that captures applicant details, allows document image uploads, and performs Aadhaar verification, creating a seamless and efficient loan processing experience. All of this is built using DronaHQ’s powerful low-code tools.

Step 1: Plan your app UI

Before building the app, it’s essential to take a step back and plan things out. Think about what kind of data you want to store in your database and what you want users to see in the app. Sketch out how many screens or tabs you’ll need, and decide what each one will do, like which tab shows the loan list, which one handles new entries, and so on. Having this clarity upfront makes the whole process smoother and helps you build a more intuitive app.

If you’re looking to get the user experience right from the start, read our Internal tool UX design guide.

For this loan application, we’ll keep things simple and clear with two main screens. The first screen displays a dashboard with a table showing loan entries, along with key status indicators indicating the number of accepted, rejected, and pending loans. The second screen will have two tabs: one for updating existing loan records using a pre-filled form, and another for a smart EMI calculator that helps users estimate monthly payments based on the loan amount and tenure. This structure keeps the app user-friendly.

Want to ensure your tables are clear and actionable? Check out our guide on Table UI design best practices.

Step 2: Integration MySQL database

Once you’ve logged into your DronaHQ account, the first step is to create a MySQL connector. Head over to the Connectors section, click New Connector, and choose MySQL. To connect it with your pre built MySQL database, you’ll need to fill in some key details like -

host
port number
database name
username
password

If you prefer, you can also use a connection string to autofill everything in one go. Not using MySQL? No worries, DronaHQ supports a wide range of data sources, including MongoDB, PostgreSQL, Firebase, and even Google Sheets. And if you’re working with a custom backend, you can opt for the REST API connector to hook up your own APIs.

You’ll also see a few options just above the “Test” button about how you want to connect, whether it’s by whitelisting IPs, using SSL, or a dynamic host setup, and more. DronaHQ has detailed documentation on all of this - DronaHQ Docs, so feel free to explore that if needed. For this Application, we’ll go ahead with a direct MySQL connection to keep things simple.

Step 3: Create an app using a template

Once your database connection is successfully set up, it’s time to start building your app. You’ll begin by creating a new app and designing its user interface. DronaHQ offers multiple ways to do this, depending on your preference:

You can manually drag and drop UI elements to design your layout from scratch.
Or, if you have an image or Figma file ready, you can simply upload an image into the AI Chat, and it will generate the interface for you automatically.
The fastest and easiest method, and the one we’ll use in this guide, is to choose from a library of pre-built templates under the “Select Ready Template” option.

DronaHQ offers a wide range of templates to get you started quickly, so you don’t have to build everything from zero. Just pick the one that fits your use case and start customising!

Step 4: Create multiple screens and a menu bar

After setting up the home screen, the next step is to create the rest of your app’s screens and a menu to move between them. In DronaHQ’s Screens section, you’ll find options like -

These can be added to any screen and reused wherever needed.

In our case, we’ll create a menu bar that shows up across all screens. This will help users easily switch between the dashboard, update form, and EMI calculator.

You can set up the navigation logic directly from the Properties panel, under the Events section. Simply define actions, such as “navigate to another screen,” when a menu item is clicked.

Step 5: Bind the table with a MySQL query

To display data in your table on the home screen, you need to bind it to the MySQL query that we set up earlier using the connector. This is how your app pulls real-time data from the database and shows it on the screen.

Select table -> Quick Select -> Connector Query -> Select Connector -> Write your query

You have two options here:

You can write your own SQL query,
Or use the “Ask AI” feature to auto-generate the query for you.

For example, a simple query like:

CopySELECT * FROM lead_capture

will fetch all the records from the “lead_capture” table and display them directly in your table component. “lead_capture” is the name of the table in our MySQL database.

From the properties section of the table, we will add an update button so that if any value needs to be updated, it can be done here.

And for each dashboard element, we will bind the data with the help of a data query we are using to insert the data into the table from the properties section.

Step 6: Create a screen for updating the form

For the second screen of our app, we’ll organize the layout using a tabbed interface by selecting the Tab template.

In the first tab, we’ll use DronaHQ’s Autogenerate Form feature. This smart tool automatically creates a form based on the columns in our database table. Once generated, we’ll bind the form fields to the data from the table, which allows the form to be pre-filled with existing values.

The best part is that the form remains fully editable, so users can easily update lead details. Once changes are made, the form can trigger an update query to reflect those changes in the database.

Step 7: Smart calculator

In the second tab of our screen, we’ll create a smart EMI calculator to help users quickly estimate their monthly loan installments. It’s simple to set up, just drag and drop two number input fields (for monthly income and monthly liabilities), add a button to run the calculation, and place a few text fields to display the results, as shown in the screenshot below -

Once the layout is ready, we’ll add a bit of JavaScript to make it functional. Here’s the logic we’ll use:

Copy
let amount_eligibility = (monthly_income - monthly_liabilities) * 10;
let emi_amount = (monthly_income - monthly_liabilities) * 0.60;
let term_in_months = 0;
if (emi_amount !== 0) {
    term_in_months = Math.round(amount_eligibility / emi_amount * 1.2);
}
return {
    amount_eligibility,
    emi_amount,
    term_in_months
};

This small script does the math for us, calculating how much loan a user is eligible for, what their EMI might be, and how long it could take to repay, on the basis of their monthly income, amount and tenure.

The results from this function are automatically shown in the text fields, giving users instant feedback as they enter their numbers. It’s a quick, user-friendly way to offer real-time financial insights right inside your app.

Step 8: Preview test publish

Once you’ve finished setting everything up, click the green Preview button in the top right corner. This lets you try out your app in real time, so you can see if the data is loading properly, make sure the screen navigation feels smooth, and double-check that the form rules and EMI calculator are working as expected. Take a moment to test everything thoroughly so you can catch any issues early. And once you’re happy with how it all looks and works, hit Publish to go live. Your app is now ready to be shared and used!

Final thoughts

There’s a lot more you can do to enhance your app based on your needs. You can add input field validations, apply custom rules, change the theme of your app, explore additional UI components, and implement custom logic to make the experience more interactive and user-friendly. It’s also a good idea to set up failure conditions to handle errors gracefully—for example, showing alerts when data fails to load or a form submission doesn’t go through.

You can refer to DronaHQ Docs to understand all these features in detail, so feel free to dive in whenever you’re ready to take things further.

What’s next?

Once your app is live, here are a few ideas to take it even further:

Add login access for reviewers to manage who can view or update records.
Track submitted loans with a charts widget to visualize approval rates, trends, and statuses.
Send confirmation emails automatically using the Gmail connector.
Implement role-based access control to restrict permissions based on user roles (admin, reviewer, etc.).