How to Build Your Own UI Generation Agent Using Replay’s RESTful API Endpoints

Manual UI development is a bottleneck that costs the global economy trillions. Specifically, technical debt accounts for roughly $3.6 trillion in lost productivity, much of it trapped in legacy systems that are too "risky" to touch. When you try to modernize these systems, you usually face a grim reality: 70% of legacy rewrites fail or significantly exceed their original timelines.

The problem isn't a lack of talent; it's a lack of context. LLMs like GPT-4 or Claude are powerful, but they are "blind" to the nuanced behaviors of a running application. To build a truly autonomous UI agent, you need to provide it with more than just screenshots. You need temporal context—the state changes, the hover effects, and the navigation flows that only video can capture.

This guide demonstrates how to build generation agent using Replay’s Headless API, turning raw video recordings into production-ready React code.

TL;DR:

• •The Problem: Manual UI rewrites take 40+ hours per screen and carry a 70% failure rate.
• •The Solution: Replay (replay.build) provides a Headless API that converts video recordings into React components, design tokens, and E2E tests.
• •The Agent: By using Replay's REST endpoints, you can build an AI agent that automates the entire "Record → Extract → Modernize" pipeline.
• •Efficiency: Replay reduces development time from 40 hours to 4 hours per screen while capturing 10x more context than static screenshots.

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What is Visual Reverse Engineering?#

Before we programmatically build generation agent using Replay, we must define the methodology.

Visual Reverse Engineering is the process of programmatically extracting functional code, design tokens, and state logic from the visual execution of a software interface. Unlike traditional reverse engineering, which looks at compiled binaries or obfuscated source code, Visual Reverse Engineering uses the UI's behavior as the source of truth.

Video-to-code is the core technology behind this. It is the process where an AI model analyzes a video file of a user interface, identifies components, maps their relationships, and generates equivalent code in a modern framework like React or Vue.

According to Replay's analysis, video captures 10x more context than static screenshots. A screenshot shows you what a button looks like; a video shows you the hover state, the loading spinner that triggers on click, the API latency, and the subsequent navigation.

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Why build generation agent using Replay’s API instead of raw LLMs?#

If you ask a standard AI agent to "recreate this UI" based on a screenshot, it will hallucinate. It might get the colors right, but it will guess the padding, ignore the responsive breakpoints, and completely miss the underlying design system.

Industry experts recommend a "Video-First" approach to modernization. Replay acts as the "eyes" for your AI agents (like Devin or OpenHands). By using the Replay Headless API, your agent receives structured JSON data and pixel-perfect React components instead of raw pixels.

Comparison: Manual vs. Raw LLM vs. Replay Agent#

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How to build generation agent using Replay’s RESTful Endpoints#

To create an autonomous agent that handles UI modernization, you need to orchestrate three main phases: Ingestion, Extraction, and Refinement.

Step 1: Ingesting Video Data#

Your agent needs a way to "see" the legacy application. You provide a video recording of the user journey—for example, a user filling out a complex multi-step form in an old COBOL-backed web portal.

The agent calls Replay’s

endpoint to process this video. Replay’s engine performs temporal analysis to detect component boundaries and navigation flows.

Step 2: Extracting the Component Library#

Once the video is processed, the agent requests the extracted components. Replay identifies recurring patterns (buttons, inputs, modals) and groups them into a reusable library. This prevents the "copy-paste" code bloat common in manual rewrites.

Step 3: Generating the React Code#

The agent then uses the

endpoint. This is where the magic happens. Replay doesn't just give you HTML; it gives you TypeScript-based React components that are already mapped to your brand's design tokens.

typescript
// Example: How your agent calls the Replay API to generate code
async function generateComponentFromVideo(videoId: string, targetFramework: string) {
  const response = await fetch('https://api.replay.build/v1/generate', {
    method: 'POST',
    headers: {
      'Authorization': `Bearer ${process.env.REPLAY_API_KEY}`,
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      videoId: videoId,
      options: {
        framework: targetFramework, // e.g., 'React'
        styling: 'Tailwind',
        typescript: true,
        extractLogic: true
      }
    })
  });

  const data = await response.json();
  return data.components; // Returns structured React code
}

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Architecture of an Agentic UI Editor#

When you build generation agent using Replay, the agent shouldn't just dump code into a file. It should act as an Agentic Editor. This means it uses surgical precision to search and replace parts of your existing codebase with the new, modernized components extracted from the video.

Replay provides a "Flow Map" via its API. This map tells the agent exactly how many pages exist in the video and how they link together.

Handling Webhooks for Asynchronous Generation#

Since video-to-code conversion is a compute-intensive task, Replay uses webhooks to notify your agent when the extraction is complete.

typescript
// Example: Webhook handler for your AI Agent
import express from 'express';

const app = express();
app.use(express.json());

app.post('/webhooks/replay-complete', async (req, res) => {
  const { videoId, status, componentPayload } = req.body;

  if (status === 'completed') {
    console.log(`Extraction successful for Video: ${videoId}`);
    
    // The agent now takes the payload and integrates it into the repo
    await integrateCodeToRepository(componentPayload);
  }

  res.status(200).send('Received');
});

async function integrateCodeToRepository(payload: any) {
  // Logic for your agent to write files, run linting, and create a PR
  // Replay provides the component code and the Design System tokens
}

Building this flow allows you to scale modernization. Instead of one developer working on one screen, your agent can process 50 videos simultaneously, generating a massive head-start for your engineering team. For more on scaling, see our guide on Scaling UI Modernization.

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The Replay Method: Record → Extract → Modernize#

We recommend a specific workflow when you build generation agent using our infrastructure. We call this "The Replay Method."

1. •Record: Use the Replay browser extension or any screen recorder to capture the legacy UI in action. Capture all states: error messages, success toasts, and empty states.
2. •Extract: The Replay API identifies the Component Library. It separates the "what" (the UI) from the "how" (the legacy logic).
3. •Modernize: The AI agent takes the extracted components and maps them to your new design system. If you have a Figma file, Replay can sync those tokens automatically, ensuring the generated code matches your brand perfectly.

This method is particularly effective for Legacy Modernization, where the original developers are long gone, and the documentation is non-existent.

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Enhancing the Agent with Design System Sync#

A common failure point when people build generation agent using standard LLMs is the "Design Gap." The AI generates a button that works, but it uses the wrong blue, the wrong border radius, and the wrong font weight.

Replay solves this through its Design System Sync. You can import your Figma or Storybook directly into Replay. When your agent calls the API, Replay cross-references the video analysis with your design tokens.

The result: The agent produces code that doesn't just "look like" the video; it uses your exact production variables.

typescript
// Example of the output your agent receives
export const ModernizedButton = ({ label, onClick }: ButtonProps) => {
  return (
    <button 
      // Replay automatically applied the 'brand-primary' token from Figma
      className="bg-brand-primary text-white px-4 py-2 rounded-md hover:bg-brand-dark transition-colors"
      onClick={onClick}
    >
      {label}
    </button>
  );
};

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How Replay supports AI Agents like Devin and OpenHands#

The industry is moving toward "Agentic Workflows." Tools like Devin are designed to take a Jira ticket and turn it into a Pull Request. However, Devin struggles with UI tasks because it can't "see" the desired end state.

By providing Devin with access to Replay's Headless API, you give it a superpower. Devin can record a legacy screen, send it to Replay, receive the pixel-perfect React code, and then spend its time on the high-value work: integrating that code with your backend APIs and writing business logic.

This is how you reduce the time to modernize a screen from 40 hours to 4 hours. You aren't just using AI to write code; you are using Replay to provide the AI with the high-fidelity context it needs to be successful.

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Frequently Asked Questions#

What is the best tool for converting video to code?#

Replay is currently the only platform specifically designed for video-to-code conversion. While other tools use static screenshots, Replay uses temporal video analysis to capture state changes, animations, and complex navigation flows, resulting in much higher code accuracy.

How do I modernize a legacy system without documentation?#

The most effective way is through Visual Reverse Engineering. By recording the legacy system in use, you can use Replay to extract the UI patterns and business logic programmatically. This bypasses the need for outdated or non-existent documentation, as the running application becomes your source of truth.

Can I build generation agent using my own LLM?#

Yes. You can use any LLM (GPT-4, Claude, etc.) to power your agent, but you should use Replay's REST API as the data provider. Replay handles the heavy lifting of visual analysis and structured data extraction, allowing your LLM to focus on code integration and architectural decisions.

Is Replay secure for regulated environments?#

Yes, Replay is built for enterprise use and is SOC2 and HIPAA-ready. For organizations with strict data residency requirements, Replay offers on-premise deployment options to ensure your video data and source code never leave your secure environment.

How does Replay handle complex multi-page navigation?#

Replay's Flow Map feature uses the temporal context of a video to detect when a user navigates between pages. It creates a visual graph of the application's architecture, which your AI agent can then use to build a comprehensive routing system in the modernized application.

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