DEV Community: Mykhailo

How to Hire Remote Front-End Developers for Your Project in 2026

Mykhailo — Tue, 05 May 2026 09:40:52 +0000

Hiring remote developers for short-term project needs has become an effective strategy for startups and small businesses to build successful products without the overhead of in-house teams. However, finding the right talent who not only meets your technical requirements but also delivers reliably can be challenging. This is especially true for companies new to outsourcing or those with less-than-ideal past experiences.

This guide will walk you through the whole process of finding and hiring front-end developers for your project, covering key aspects such as skills, rates, and collaboration.

Benefits of Hiring a Remote Front-End Developer for Your Project

Let’s start by understanding the key benefits of hiring remote developers and situations when it works best.

The key advantages of working with remote specialists include the following:

Cost-efficiency: Statistics show that hiring a remote developer can be approximately 1.5–3× more cost-effective than hiring in-house. This advantage comes not only from reduced overhead costs, but also from more flexible engagement models and differences in global market rates.
Faster hiring process: Remote engineers can often begin working on a project within a week. This is a strong alternative to lengthy recruitment processes that require additional time, cost, and effort for screening and expertise validation.
Access to a global talent pool: You can hire from regions with more competitive rates and gain access to highly skilled specialists who may be difficult to find or afford in local markets. This approach significantly expands your talent pool, making it easier to find niche expertise or senior-level professionals.
Flexibility and scalability: Remote teams scale up and down according to your needs. The greater benefit is that you can hire for specific tasks or timeframes instead of committing to a full-time role.

As a result, you accelerate time-to-value, avoiding bureaucratic processes and focusing only on progress.

Key Front-End Technologies Your Developers Should Know

While the exact tech stack depends on your project requirements, there are several core technologies that front-end developers are generally expected to know. Let’s break them down.

Programming Languages

Front-end programming languages are the core building blocks. Small projects such as prototypes, MVPs, or simple websites are typically built with JavaScript, while more complex applications often rely on TypeScript for added safety and scalability.

However, many teams use both, since TypeScript ultimately compiles down to JavaScript. As a result, familiarity with both languages is a valuable advantage for front-end developers.

Frameworks & Libraries

These tools are used for building scalable, reusable, and efficient user interfaces. There are different frameworks, and their number is growing every year, but React, Vue, and Angular remain the most popular.

The choice between React and Angular is the most common case for production-grade applications. React is typically used for small to medium-sized projects, while Angular is used for more complex applications with strict security requirements.

Make sure to define which technology works best for your project since most front-end developers specialize in one primary framework.

Styling Tools

Tools such as CSS and Tailwind CSS are used to design layouts and manage styles. While CSS is basic in front-end development, Tailwind CSS is commonly used in modern projects (especially with frameworks like React) to build UIs faster and more consistently.

Data Communication

Front-end engineers should have experience working with modern APIs to enable seamless interaction between user interfaces and back-end systems. In practice, this means being able to consume and integrate services such as REST APIs or GraphQL.

While front-end developers are not typically responsible for building APIs, they should understand how client–server communication works, handle data formats like JSON, manage authentication flows, and deal with loading states or errors.

Essential Skills to Look for When Hiring Remote Front-End Developers

In addition to technical knowledge, front-end developers must also possess other skills important for building software products. They include the following:

Communication: It’s an essential skill for any remote developer, reducing misunderstandings and enabling smooth collaboration across distributed teams. Make sure the developers you will work with can clearly express their ideas and ask clarifying questions when needed.
Time management: Another crucial competence that helps deliver work on time by planning tasks and meeting deadlines. This is especially important if your team is distributed between different time zones.
Familiarity with collaboration tools: Collaboration apps provide efficient teamwork, clearer communication, and smoother project coordination throughout the development lifecycle. This includes experience with tools for version control, such as Git and platforms like GitHub or GitLab for code review, as well as communication apps like Slack or Microsoft Teams.
Cybersecurity awareness: Secure coding best practices are non-negotiable for any production-ready application. Validate a developer’s understanding of common security risks and their ability to prevent them in real-world scenarios. This includes proper input validation and sanitization, safe handling of user data, secure authentication, and authorization practices.
Understanding the importance of UX: It’s crucial that front-end developers can do more than simply translate designs from Figma into code. They should also evaluate how the interface performs in real use, ensuring that users can navigate the product easily, understand its structure intuitively, and interact with it without friction.

Prioritize what skills are the most important for your project and company culture, and make sure to include them in the requirements.

Where to Find Front-End Developers

Once you have a clear portrait of the front-end specialist you need, you can proceed with the search. There are several options where you can actually find remote developers.

Freelance platforms

Platforms like Upwork, Fiverr, or Toptal help business owners connect with software engineers all over the world. Basically, all you need to do is post your job and review candidates who apply.

Pros:

Relatively low cost compared to traditional hiring
Large talent pool with fast access to available developers

Cons:

Security and trust risks when sharing code and access with external developers
Integration challenges when coordinating multiple freelancers in one project
Inconsistent quality assurance, with no guaranteed standard of review or testing

Development communities

Another way to find developers is through community platforms such as Reddit or Stack Overflow. This is an easy way to find someone with similar values and establish interaction without the hassle of bureaucracy, but this approach carries many risks and is mostly based on trust.

Pros:

Access to motivated talent from niche tech communities
Low cost or flexible partnerships, often more affordable than traditional hiring models

Cons:

Limited scalability, making it harder to quickly build large teams
Harder to compare candidates objectively, since profiles are less standardized
Reliability and security risks, as community reputation doesn’t always reflect professional performance

Outsourcing companies

Finally, you can opt for a more secure option of hiring remote front-end engineers from an outsourcing company like Seedium. This approach provides more guarantees in the delivery and professionalism of the engineers you work with.

Pros:

Proven talent pool with pre-vetted and experienced developers
Easier scalability, as you work with an already established and structured team
More consistent quality and accountability due to internal processes and team coordination
Greater reliability, supported by formal contracts and the outsourcing company’s reputation

Cons:

Slightly higher cost than freelancers

Whichever option you choose, the main thing is to develop an effective approach to team management and quality assessment.

Continue reading on our website to explore the cost of hiring front-end developers in 2026.

7 Tech Startup Trends: From Vibe Coding to Cybersecurity

Mykhailo — Mon, 27 Apr 2026 13:57:55 +0000

Key takeaways:

The key tech startup trends include vibe coding, AI agents, data as a revenue stream, vertical solutions, hiring dedicated teams for product development, and an increased focus on AI ethics and security.
The development of the startup market is primarily focused on industry-specific and AI-related solutions, driven by user demand and investment trends.
AI has a significant impact on startup technology trends, influencing the full product lifecycle, from development to user engagement and customer support.

In more than 10 years in business and software development, I have been involved in the creation of over 100 startups as a founder, partner, or engineering services provider. One thing I have learned is that understanding the business and tech landscape you’re working in is crucial to your success.

In this article, my team and I have gathered the top tech startup trends to help business leaders keep up with the latest developments and innovations, shaping the startup ecosystem.

Key Startup Industry Trends: Market Overview 2026

The startup market dynamics shift toward a more strategic, sector-focused, and globally distributed ecosystem. Investors are becoming more selective and choosing startups that can show clear product-market fit and well-defined business models. Operational efficiency, intelligent automation, and ESG alignment now play a key role in shaping which startups thrive.

According to BestBrokers research, in 2025, AI-related startups raised 52.7% of global venture capital funding. This year, this trend is expected to accelerate further. Other popular directions include FinTech, HealthTech, ClimateTech, and Defense.

Although the startup ecosystem is increasingly expanding into local and regional markets, North America remains the primary global hub.

7 Technology Trends for Startups and How to Apply Them

Key technology trends shaping startups include vibe-coding, AI agents, monetizing data as a revenue stream, industry-specific solutions, dedicated teams to tackle talent gaps, AI ethics and explainability, and strengthened cybersecurity. Let’s talk about each of these trends and ways to adopt them in your business strategy.

1. Vibe Coding Enables Faster Time-to-Market

The rise of vibe coding has created an environment where startups can build PoCs and MVPs without large teams or significant investments. Today, even solo founders can quickly create a prototype using AI to test their ideas or attract investors. This significantly speeds up the product lifecycle as you can gather feedback faster, adapt more effectively, and release market-ready solutions in less time than traditionally.

In 2025, Y Combinator Managing Partner Jared Friedman and CEO Harry Tan reported that approximately 25% (one in four) of the Winter 2025 startups have 95% AI-generated codebases. However, they also noted that for the most part, these startups were built by tech-savvy founders who have a clear understanding of which parts of development can be automated using AI.

While vibe coding is a great way to kick off startup product development, it’s crucial to understand the limitations of this approach. Vibe-coding tools are prone to producing poorly structured code, unclear architecture, and security vulnerabilities. As a result, you may end up with an app that works in the beginning, but is difficult to scale and maintain in the long run. That’s why engaging experienced software developers is essential to secure and stabilize your vibe-coded application.

Case study: Scaling a vibe-coded SaaS application

At Seedium, we help early-stage startups scale their vibe-coded apps into market-ready products. We use a smart approach to ensure you get the maximum value from your initial investments without needing to build everything from scratch. Our engineers begin by analyzing your system and prioritizing key modules for improvement. Here is an example of a project we worked on.

The client contacted us to stabilize a vibe-coded AI management platform. It was initially built with no-code tools and, over time, began experiencing significant output delays that disrupted workflows. The platform accumulated substantial technical debt due to a lack of coding standards.

After a system audit, we implemented the following solutions to secure the platform:

Refactored the codebase and reduced technical debt by 30% within the first 1.5 months.
Implemented coding standards to simplify development and maintenance.
Optimized the architecture to improve performance.
Balanced workloads to ensure no single component became a bottleneck.
Restructured the back end for greater stability.
Implemented new features and integrations.

As a result, the platform became more stable and scalable, allowing the client to develop new features without breaking the system or causing delays. Users shared encouraging feedback. They recognize the platform’s strong potential and look forward to upcoming updates.

Read the full case study: Scaling a Vibe-Coded SaaS Application

How to apply this trend in your strategy

Use vibe coding tools to test new concepts and features. Build proofs-of-concept to pitch to investors and gather early customer insights. The key is to use vibe coding to validate and refine ideas before committing significant resources to full-scale development.

2. AI Agents Automate Complex Workflows

According to MarketsandMarkets, the AI agent market is expected to expand more than sixfold between 2025 and 2030. The rapid development and accessibility of agentic AI has made it an affordable tool not only for enterprises but also for startups.

Early-stage businesses actively use AI coding assistants such as Cursor or GitHub Copilot to speed up product development workflows. With the right settings, such tools can effectively handle repetitive tasks, including generating boilerplate code, writing unit tests, formatting code, creating templates, and integrating standard APIs.

Our latest State of SaaS Report found that more than half of professional developers use AI coding assistants daily in their jobs.

*Download report here

Mature startups rely on AI agents to automate routine internal tasks such as data entry, email response, scheduling, and report generation. Using such tools helps small teams scale operations without significant investment.

Unlike AI-based chatbots, AI agents can perform multi-step tasks. For example, they can handle new user onboarding by verifying accounts, guiding setup, sending personalized tutorials, collecting feedback, and generating analytics. This allows startups to compete with larger companies without adding headcount.

How to apply this trend in your strategy

Define what repetitive tasks in your workflow can be automated without compromising quality. The truth is that you’ll still need to engage human experts at some point to review AI outcomes, guide models, or solve complex requests. So, focus on striking the right balance.

Start with automating one task and move forward only after validating success criteria such as accuracy, timeliness, and error-free execution.

3. Data Drives New Revenue Streams

In the age of AI, high-quality data is more valuable than gold. Companies need clean, well-organized datasets to implement AI and machine learning effectively, yet many struggle with in-house data management. According to Salesforce, 62% of tech industry leaders cite disconnected data sources as one of their top business challenges.

This shift has led companies to recognize data as a valuable asset capable of generating direct or indirect income, giving rise to Data-as-a-Service (DaaS) as one of the latest tech startup trends. DaaS provides organizations with ready-to-use, curated datasets delivered via APIs or cloud platforms.

Synthetic data is an emerging driver within this trend, especially for tech startups. By generating artificial yet realistic datasets, startups can train AI models, test products, and simulate different scenarios. This not only accelerates product development but also opens new revenue opportunities, such as providing high-quality synthetic datasets to other companies.

According to Grand View Research, the global DaaS market is expected to grow at a CAGR of 28.1% from 2024 to 2030.

How to apply this trend in your strategy

You can use a DaaS platform to power your AI initiatives or monetize your datasets, provided they are clean, well-organized, and properly governed. Another way is building analytics platforms that deliver actionable insights and decision support to other businesses.

4. Vertical Solutions Address Specific Industry Challenges

The trend toward vertical solutions is gaining strength, driven by growing demand for industry-specific tools, regulatory complexity, and the need for tailored workflows that generic software can’t address.

Founders of vertical SaaS products often come from the industries they serve. This means they have unique insights that enable them to create more effective and targeted products. Using vibe coding, they can rapidly prototype and iterate on these solutions, competing with established teams.

AI makes verticalization even more attractive. Investors see AI-powered vertical solutions as differentiated and defensible because of the combination of domain expertise and smart automation. These applications are more likely to achieve product-market fit and scale efficiently within their niche.

Case study: Building a communications platform for real estate investors

At Seedium, we help businesses build both horizontal and vertical solutions. With commercial experience across 15+ industries, our engineers apply their deep technical expertise and industry-specific insights to design scalable, reliable, and innovative software solutions.

For example, several years ago, we helped the client rebuild their communication platform. The main goal was to turn the beta version into a stable, full-scale application, avoiding any system disruptions during the modernization process.

Solutions we provided:

Implemented microservice architecture to improve scalability.
Performed code refactoring and migrated from JavaScript to TypeScript to improve product stability and simplify long-term support.
Provided security improvements.
Set up CI/CD for faster and more stable feature delivery.

As a result, feature development became ~10–15% faster. The platform became more stable and scalable and successfully serves over 10,000 businesses worldwide.

Read the full case study: Building a Communications Platform for Real Estate

How to apply this trend in your strategy

If you’re an early-stage startup, make sure your product idea addresses a real, specific problem for your target audience. Engage with industry leaders to identify gaps and validate your concept, ensuring your solution delivers tangible value.

For more mature startups, the focus shifts to scaling within specific industries by building tailored features, workflows, and integrations that meet the unique needs of their niche markets.

Read the full article on our website to explore all the trends and insights shaping the future of tech startups.

10 Steps to Migrate Your Vibe-Coded App to Production with AWS

Mykhailo — Mon, 27 Apr 2026 13:16:14 +0000

Optimized for speed, AI and no-code tools can help you vibe code a prototype in days. The challenge is taking it to production, where it must remain reliable under real-world conditions. This requires a scalable architecture and secure infrastructure with proper testing and observability in place.

In this article, we’re sharing our experience migrating vibe-coded apps to back-end infrastructure using AWS as an example. Find a step-by-step guide to help you transition to production without any bottlenecks.

1. Define Your Database Structure

Before doing any migrations, you should map out how your data translates into production-grade storage and schemas in AWS. For example:

Users table → RDS (PostgreSQL)

Cache → Redis

Images → S3

Vibe-coded apps often have mixed logic and inconsistent data formats. So you might need to normalize data by defining entities and relationships and removing implicit structure from code. This will add clarity and structure to your database.

Also, don’t forget about security and access control at this stage. Use IAM roles, encryption (at rest + in transit), and access policies.

2. List Production Requirements

Deciding on the key aspects of your system helps you to choose the right services and make the right configurations. Here are some metrics you need to consider:

Scalability: How many users, expected load spikes
Availability: SLA (Service Level Agreement) and SLO (Service Level Objective) targets
Security: Data sensitivity, compliance needs (e.g., GDPR, HIPAA, SOC)
Observability: Logging, metrics, monitoring A clear understanding of these factors reduces technical debt, improves reliability, and ensures your architecture can evolve with your product.

3. Choose Your Back-End Architecture

Choosing architecture is the step where you decide how your application will be structured, run, and scaled in AWS.

At this stage, you decide on the overall architecture style, such as a monolith or microservices, serverless, or an event-driven approach. Each option has trade-offs: a monolith is simpler to build and deploy, while microservices and event-driven systems are more scalable and flexible but introduce additional complexity.

For many modern AWS setups, teams often choose serverless or container-based architectures to balance scalability and operational overhead.

Another key part of architecture is defining how components communicate with each other. This includes choosing between synchronous communication (like REST APIs) and asynchronous patterns (such as queues and events using services like SQS or SNS).

4. Set Up Your AWS Environment

The best practice is to create separate environments for development, staging, and production. This ensures that experimentation, testing, and real user traffic are isolated from each other. Next, configure IAM (Identity and Access Management) roles and permissions. This is a critical security layer that controls who can access what within your AWS environment. This typically includes roles for developers, CI/CD pipelines, and application services, and assigns precise permissions to resources like databases, storage, and compute services. Proper IAM setup helps prevent accidental changes and protects sensitive data. Applying the principle of least privilege here is essential for maintaining a secure environment.

Finally, you set up billing alerts and budgets to monitor and control costs. AWS usage can scale quickly, so it’s important to track spending from the start.

5. Refactor Your App

Vibe-coded apps are often built without a strict structure. So you’ll need to clean up the codebase by introducing clear patterns, separating concerns, and improving maintainability.

Here are some of the most important improvements:

Proper error handling and logging. Each request and error should be tracked, ideally with context such as user ID, request ID, and timestamps. This makes it much easier to debug issues and monitor system behavior.
Making services stateless. Your app shouldn’t rely on local memory or disk to store important data between requests. Instead, all state should be stored in external systems like databases or object storage (for example, S3 or DynamoDB).
Securing database connections. Use connection pooling to reuse existing connections instead of creating new ones for every request. Additionally, database credentials and connection strings should be stored securely rather than hardcoded in your application.

It’s crucial to understand that attempting to refactor an entire system at once introduces significant risk. Refactoring should be performed incrementally, allowing each change to be tested and validated before moving forward. Before starting any refactoring, we strongly recommend establishing a solid test foundation. At a minimum, unit tests should be implemented, with coverage focused on critical server-side logic. This ensures that changes don’t introduce regressions.

6. Containerize Your App (If Needed)

Containerizing your app helps standardize how it runs across different environments. In most cases, this is done using Docker. However, containerization is not always required. It becomes necessary when deploying to platforms like AWS ECS or AWS EKS (Kubernetes), where containers are the core unit of deployment.

If you’re using a serverless architecture such as AWS Lambda, containers are typically not needed, as AWS manages the underlying infrastructure for you.

In practice, if your app is simple and serverless already meets your needs, it’s often better to skip containerization to make the system easier to manage.

7. Configure Networking & Security

Security settings involve both defining how your system communicates internally and how it is protected from external threats. The foundation of this setup is the Virtual Private Cloud (VPC), which acts as an isolated network environment for your resources.

A key best practice is to place your databases in private subnets, ensuring they are not directly accessible from the Internet. Instead, they can only be accessed by internal services within the VPC, significantly reducing the attack surface.

Finally, you should enable secure communication across your system by configuring HTTPS using AWS Certificate Manager (ACM). Certificates from ACM can be attached to services like CloudFront (CDN) or an Application Load Balancer (ALB), ensuring that all traffic between users and your application is encrypted.

8. Implement CI/CD

Continuous Integration and Continuous Deployment turn your deployment process from a manual task into an automated pipeline.

What you need to do is to connect your code repository (for example, GitHub) to a CI/CD system such as AWS CodePipeline or GitHub Actions. Once connected, you automate the build process. This step compiles your code, installs dependencies, and prepares artifacts.

Next, you integrate automated testing into the pipeline. This includes:

Unit tests to validate individual components
Integration tests to verify how different parts of the system work together This way, you’ll be able to catch bugs early and prevent broken code from reaching production.

9. Set up Observability

The foundation of observability consists of three key pillars: logging, metrics, and tracing.

Logging captures detailed records of events and errors within your application.
Metrics provide insights into system performance, such as CPU usage, memory consumption, request latency, and error rates.
Tracing tracks how a single request flows through different services, helping you identify bottlenecks or failures across the system.

In AWS, you can use Amazon CloudWatch as the central place to collect logs and metrics. CloudWatch allows you to aggregate logs from different services, visualize metrics, and create custom dashboards to monitor system health.

10. Test and Deploy to Production

Never neglect testing before going live. Run unit and integration tests to ensure your core logic works correctly, followed by load and performance tests to verify the system can handle real-world traffic and scale under pressure.

Once everything is verified, perform data migration using an ETL process. This includes extracting data from your existing system, transforming it to match your new schema, and loading it into your production databases while validating integrity.

Deploy using safe strategies like blue/green or canary releases. This way, you can gradually roll out changes and minimize risk by allowing quick rollback if issues arise.

Test your vibe-coded app for scalability in 5 minutes. Find weak areas that need improvement and advise on how to do it right. Take the test.

Read the full article on our website, including the case study and common mistakes to avoid when moving from vibe coding to production.

How to Build AI Agents for Your Business

Mykhailo — Wed, 22 Apr 2026 09:31:41 +0000

Implementing AI agents offers a new level of automation for businesses. It’s no wonder that interest in this topic is growing, and more and more business and tech leaders are wondering how they can build an effective agent.

In this post, I’ll share my team’s experience. I hope you find it insightful.

Key Components of AI Agents

AI agents have three key architectural components:

LLMs
Contextual memory
Functions, tools, and routing capability

LLMs

Large language models act as the brains of agentic systems. They take user input and generate a structured plan outlining the sequence of actions required to achieve the user’s goal.

Contextual Memory

This is a layer where information about the previous agent’s operations is stored. AI agents use short-term and long-term memories to improve performance and user experience.

Short-term memory is limited within a single session context. Its main goal is to keep an ongoing conversation and provide relevant outcomes to the user within the current interaction.

Long-term memory allows agents to learn over time and maintain persistent experience across sessions. It’s usually backed by vector databases or structured storage.

Functions, tools, and routing capabilities

Integration with other tools and systems is a crucial part of developing an AI-based agent. This includes databases, business services (CRM, ERP), code execution environments, etc.

Functions define what the agent is allowed to do in a structured way. The routing layer selects the appropriate tool or function based on the user’s intent and current context.

Together, they work in a continuous flow where the model interprets the request, the routing layer determines the best action path, and functions execute the required operations.

How to Create an AI Agent for Business: Step-by-Step Guide

Creating AI agents that provide real value requires a combination of strong business planning and deep technical expertise. Let’s break the AI agent development process into clear, practical stages.

1. Define Business Goals and Use Cases

The main secret of successful AI implementation is to start with a clear, narrow business goal. Many begin mapping out the features of AI agents instead of defining the specific problem to solve. This is a big mistake.

First, identify which manual processes within your team would genuinely benefit from automation. This could be repetitive data entry, routine customer support inquiries, report generation, etc.

Next, check whether the process can be effectively handled by an AI agent: if it has clear steps, available data, and predictable outcomes.

Finally, evaluate whether you actually need a full agent or if a simpler bot would be enough to solve the problem. For example, a simple chatbot can handle FAQ responses or password resets, while a full AI agent may be needed for processing multi-step customer requests by coordinating between systems.

2. Choose a Tech Stack

If you have decided to move on with AI agent development, it’s time to choose the right tools to build and deploy your solution. The choice greatly depends on the type of agent you want to build and the level of complexity it needs to handle.

For the core intelligence layer, most software developers use a large language model API such as OpenAI or Anthropic.

For orchestration, you typically need a framework that manages agent workflows, tool use, and multi-step reasoning. Common choices include LangChain, LlamaIndex, or a lighter custom Python-based orchestration if you want more control.

For memory and data storage, vector databases like Pinecone, Weaviate, or FAISS are used for semantic memory, while traditional databases like PostgreSQL handle structured data and state.

For back-end infrastructure, Python is the dominant choice due to its AI ecosystem, with FastAPI or Flask being commonly used for serving agent APIs. In production setups, Docker and Kubernetes are often added for scalability.

However, low/no-code AI agent development platforms are often the best starting point to avoid complex development processes.

3. Prepare Your Data

Data is the fuel for your AI agent, on which the quality of outcomes depends. So, make sure your datasets are clean, up-to-date, and consistent. In practice, this often means preprocessing work such as removing duplicates, eliminating outdated records, standardizing labels, date formats, and naming conventions. Modern agentic data intelligence platforms can significantly simplify this stage by automating much of the cleaning, structuring, and integration process.

4. Build Core Agent Logic

To create an agentic AI workflow, you need to decompose the process into a clear sequence of steps that an agent can follow to reach the desired outcome.

For example, to process a customer refund request in an e-commerce system, the workflow can be broken down into steps such as:

Identify the user’s intent
Verify the customer and locate the order in the CRM
Check refund eligibility based on policies
Determine the appropriate action (full refund, partial refund, or rejection with explanation)
Execute the operation in the payment system and update the order status
Generate a confirmation message for the customer

Now, let’s turn it into a clear system logic. A typical AI agent workflow can be implemented as an event-driven pipeline with clear control flow:

AI Agent Logic

Trigger Layer: The workflow starts from an external event, such as a user message.
Planning/Reasoning Layer: The agent processes the input using an LLM to interpret intent, construct context, and generate a structured execution plan (often in JSON or function-call format).
Execution Layer: The orchestrator executes the planned steps by calling external services and APIs (e.g., databases, CRM systems, payment gateways, etc.).
State & Verification Layer: Each tool response is validated against expected schemas or business rules.
Control Layer: If validation fails or new information changes the context, the agent re-enters the planning stage to adjust the execution path or retry specific steps.
Observability Layer: All steps, including prompts, tool calls, responses, errors, and decision points, are logged and traced for debugging, evaluation, and performance monitoring (often via OpenTelemetry or similar systems).

From there, you can build the key elements of your agentic system.

5. Implement Memory and Context Management

At this step, you need to implement how the system uses information beyond a single interaction to produce more consistent performance over time.

In practice, this often includes a context window manager that prepares inputs for the LLM, a storage layer such as a database or vector store for long-term memory, and a retrieval mechanism that surfaces the most relevant information based on similarity or rules.

The key challenge is to ensure the agent has enough context to make informed decisions without overwhelming it with irrelevant or outdated data. One of the solutions is to store long-term data externally, then only pull back relevant pieces based on similarity search (embeddings) and simple filters like time or type.

6. Test and Refine

After all the above, it’s time to ensure that the agent provides the results you expect. Define key test scenarios based on real user tasks. Verify that the agent calls the right APIs, sends correct parameters, and handles failures. This way, you will test not only reasoning but the full workflow.

Finally, run regression testing continuously, especially after prompt changes or model updates, to ensure behaviour doesn’t degrade over time.

7. Deploy and Integrate

Once everything is tested and verified, you can deploy an AI agent to production. A key part of this phase is defining clear interfaces between the agent and external systems. This includes authentication, rate limits, input/output schemas, and error handling to ensure predictable and secure interactions.

Best practices also include introducing monitoring, logging, fallback behaviour, and human-in-the-loop escalation for sensitive or uncertain actions.

That’s it. Your AI agent is ready!

Check out AI agent case studies and AI agent development best practices here.