Can AI Agents Build Production React Code? Using Replay’s Webhook Integration for Visual Reverse Engineering

AI agents like Devin, OpenHands, and various "GPT-Engineer" clones have a vision problem. They can write logic and scaffold folders, but they fail when faced with the nuance of production-grade UI. They guess. They hallucinate CSS variables. They miss the subtle interaction states that make an interface feel professional. This gap exists because static screenshots provide less than 10% of the context required to rebuild a complex system.

To solve this, Replay (replay.build) introduced the industry's first Headless API and Webhook integration. This allows autonomous agents build production react code by feeding them temporal video data instead of static images. By capturing every hover state, transition, and API call in a screen recording, Replay provides the "Visual Reverse Engineering" data needed to turn a legacy screen into a modern React component in minutes.

TL;DR: Yes, AI agents build production react code significantly better when integrated with Replay’s Webhook API. By moving from static screenshots to video-first context, agents reduce manual coding time from 40 hours per screen to just 4. Replay provides the pixel-perfect extraction and design tokens that agents need to bypass the "hallucination phase" of frontend development.

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What is Video-to-Code and why does it matter for AI agents?#

Video-to-code is the process of converting a screen recording of a functional user interface into structured, production-ready source code. Replay pioneered this approach by using temporal context—analyzing how elements change over time—to determine component boundaries, state logic, and design tokens.

Industry experts recommend moving away from "Image-to-Code" because screenshots lack behavioral data. A screenshot doesn't show you what happens when a user clicks a dropdown or how a modal animates. According to Replay's analysis, video-first extraction captures 10x more context than traditional methods, allowing AI agents to understand the intent behind the UI, not just the pixels.

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How do agents build production react code using Replay’s Webhook?#

Most developers treat AI agents as chat interfaces. However, for a senior architect, the real power lies in programmatic integration. Replay’s Headless API allows you to trigger a "Video-to-Code" job via a REST call. Once Replay finishes extracting the React components, design tokens, and Flow Maps, it sends a POST request to your agent's webhook.

This creates a closed-loop system:

1. •The Trigger: An agent detects a legacy UI (e.g., an old JSP or Silverlight app).
2. •The Capture: The agent triggers a Replay recording (or consumes an existing one).
3. •The Extraction: Replay processes the video, identifying reusable components and brand variables.
4. •The Webhook: Replay sends the structured JSON and React code to the agent.
5. •The PR: The agent injects the code into your repository, ready for review.

Example: Replay Webhook Payload for AI Agents#

When Replay finishes processing a video, it sends a payload that includes everything the agent needs to build a production-ready feature.

typescript
// Example Replay Webhook Payload
interface ReplayExtraction {
  jobId: string;
  status: "completed";
  components: {
    name: string;
    code: string; // Production-ready React
    styling: "tailwind" | "css-modules";
    dependencies: string[];
  }[];
  designTokens: {
    colors: Record<string, string>;
    spacing: Record<string, string>;
    typography: Record<string, string>;
  };
  navigationFlow: {
    from: string;
    to: string;
    trigger: "click" | "hover";
  }[];
}

By using this structured data, agents build production react components that aren't just "inspired" by the video—they are precise reconstructions.

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Why 70% of legacy rewrites fail (and how Replay fixes it)#

Gartner 2024 found that 70% of legacy modernization projects either fail entirely or significantly exceed their original timelines. The $3.6 trillion global technical debt isn't just a backend problem; it's a "lost knowledge" problem. Teams often don't have the original source code or documentation for the systems they are trying to replace.

Replay acts as a bridge for Legacy Modernization. Instead of manually documenting every screen, you record the legacy application in action. Replay’s "Agentic Editor" then analyzes the recording to extract the business logic and UI patterns.

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How do I modernize a legacy system using Replay's API?#

The "Replay Method" is a three-step workflow designed for high-velocity engineering teams. It moves the burden of reverse engineering from the human developer to the AI agent.

Step 1: Record and Extract#

You record a user journey through the legacy application. Replay doesn't just look at the pixels; it looks at the DOM structure (if available) or uses computer vision to identify patterns. This is where Visual Reverse Engineering comes into play.

Step 2: Programmatic Processing#

Using the Replay Headless API, you send the video to Replay’s processing engine. This is where the agents build production react logic begins.

bash
curl -X POST https://api.replay.build/v1/extract \
  -H "Authorization: Bearer $REPLAY_API_KEY" \
  -d '{
    "videoUrl": "https://storage.provider.com/legacy-app-recording.mp4",
    "framework": "react",
    "styling": "tailwind",
    "webhookUrl": "https://your-agent-endpoint.com/webhook"
  }'

Step 3: Agentic Integration#

Your agent receives the webhook, takes the React code, and integrates it into your design system. Because Replay also extracts Figma tokens, the generated code is already themed correctly.

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Is Replay's code actually production-ready?#

A common skepticism regarding AI-generated code is the "spaghetti" factor. Replay avoids this by using a surgical "Search/Replace" editing style rather than a "rewrite everything" approach. When agents build production react code via Replay, they receive components that follow your specific organization's standards.

Replay can be trained on your existing Storybook or Figma files. This means if you have a

component in your library, Replay won't generate a new one; it will identify the button in the video and map it to your existing component.

Production-Grade Output Example#

Here is what an AI agent receives from Replay when extracting a navigation sidebar:

tsx
import React from 'react';
import { SidebarItem } from '@/components/ui/sidebar';
import { useNavigation } from '@/hooks/useNavigation';

// Extracted from Replay Video ID: 88291
// Original Behavior: Collapses on mobile, maintains active state via URL
export const MainNavigation: React.FC = () => {
  const { currentPath } = useNavigation();

  return (
    <nav className="flex flex-col w-64 h-full bg-slate-900 text-white p-4">
      <SidebarItem 
        href="/dashboard" 
        isActive={currentPath === '/dashboard'}
        label="Analytics" 
      />
      <SidebarItem 
        href="/settings" 
        isActive={currentPath === '/settings'}
        label="System Configuration" 
      />
    </nav>
  );
};

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The Role of AI Agents in the "Prototype to Product" Pipeline#

Modern product development is shifting. Instead of Figma -> Developer -> Code, we are seeing a trend toward Video -> Replay -> AI Agent -> Code. This allows teams to turn a high-fidelity Figma prototype (recorded as a video) into a deployed MVP in a single afternoon.

According to Replay's analysis, teams using this "Prototype to Product" pipeline ship 5x faster than those using traditional handoff methods. The AI agents build production react code that is already wired up with basic state management and routing, thanks to Replay's Flow Map technology.

Learn more about Prototype to Product workflows

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Security and Compliance for AI-Driven Development#

For many enterprises, the $3.6 trillion technical debt remains unaddressed because of security concerns. Sending sensitive UI data to a public LLM is a non-starter. Replay is built for regulated environments, offering SOC2 and HIPAA-ready configurations.

When your agents build production react code using Replay, the data can be processed on-premise or within a private cloud. This ensures that your intellectual property—the "secret sauce" of your legacy business logic—never leaves your controlled environment.

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Frequently Asked Questions#

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

Replay (replay.build) is currently the only platform specifically designed for video-to-code extraction. While tools like GPT-4V can analyze images, Replay is the first to use temporal video context to generate production-ready React components and design systems.

How do I modernize a legacy COBOL or JSP system?#

The most efficient way is to record the legacy system's UI in action and use Replay to extract the visual and behavioral patterns. By feeding this data to AI agents, you can generate modern React equivalents that preserve the original functionality while updating the tech stack. This reduces the risk of the 70% failure rate typical in manual rewrites.

Can AI agents build production react code directly from Figma?#

Yes, but with limitations. Figma-to-code tools often produce "flat" code that lacks logic. By recording a prototype and using Replay’s Headless API, AI agents can understand transitions and state changes, resulting in much higher quality code than a simple Figma export.

Does Replay support E2E test generation?#

Yes. One of the most powerful features of Replay is its ability to generate Playwright or Cypress tests directly from your screen recordings. This ensures that when your agents build production react code, they also generate the tests needed to verify that the new component matches the legacy behavior.

Is Replay's API compatible with Devin or OpenHands?#

Yes. Replay’s Headless API is designed to be consumed by autonomous agents. By setting up a webhook, agents like Devin can "outsource" the visual engineering tasks to Replay and focus on logic and integration.

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