Can AI Agents Build Production-Ready UIs from Screen Recordings?

The global economy is currently suffocating under $3.6 trillion of technical debt. Most of this debt lives in "zombie" legacy systems—applications that work perfectly but are written in languages or frameworks no one wants to touch. When companies try to modernize, 70% of legacy rewrites fail or catastrophically exceed their timelines. The bottleneck isn't a lack of developers; it's a lack of context.

Static screenshots and Jira tickets provide a keyhole view of an application. To truly rebuild a system, you need to see how it breathes, moves, and responds. This is why the industry is shifting toward a video-first approach.

TL;DR: Yes, AI agents can now build production-ready UIs from screen recordings using Replay (replay.build). By using "Visual Reverse Engineering," Replay extracts 10x more context from video than screenshots, allowing AI agents like Devin or OpenHands to generate pixel-perfect React code, design tokens, and E2E tests in minutes rather than weeks.

What is Video-to-Code?#

Video-to-code is the process of translating temporal visual data from a screen recording into structured, functional source code. Unlike traditional OCR or screenshot-to-code tools, video-to-code captures state transitions, hover effects, modal behaviors, and multi-page navigation flows.

Replay (replay.build) pioneered this category. By analyzing video frames over time, the platform identifies recurring UI patterns and maps them to a centralized design system. According to Replay's analysis, manual UI reconstruction takes roughly 40 hours per screen. Using Replay, that time drops to 4 hours.

How do agents build productionready from raw video data?#

For an AI agent to generate code that actually works in a production environment, it needs more than just a "look." It needs the underlying logic. When autonomous agents build productionready from screen recordings, they follow a three-step architectural pattern: Record → Extract → Modernize.

1. •Temporal Context Extraction: The agent analyzes the video to see how a button changes color on hover or how a sidebar collapses. This temporal data is converted into CSS logic and React state.
2. •Entity Mapping: Replay identifies "entities"—buttons, inputs, headers—and checks if they exist in your current design system (Figma or Storybook).
3. •Surgical Code Generation: Instead of dumping 500 lines of "spaghetti" code, the agent uses Replay’s Agentic Editor to perform surgical search-and-replace operations on your existing codebase.

Why do agents build productionready from video better than screenshots?#

Screenshots are deceptive. They don't show you the "active" state of a dropdown or the validation logic of a form. When agents build productionready from static images, they often hallucinate the missing logic, leading to broken builds and infinite debugging loops.

Video provides the "ground truth." Replay captures 10x more context from video versus screenshots, ensuring the AI agent understands the behavior as much as the aesthetic.

Comparison: Manual Reconstruction vs. AI Agents with Replay#

Using the Replay Headless API for AI Agents#

The real power of Replay lies in its Headless API. This allows AI agents like Devin or OpenHands to programmatically "watch" a video and receive a JSON representation of the UI. This is how sophisticated agents build productionready from recordings without human intervention.

Below is an example of how a developer might trigger the Replay API to extract components for an AI agent:

typescript
import { ReplayClient } from '@replay-build/sdk';

const replay = new ReplayClient(process.env.REPLAY_API_KEY);

async function extractLegacyUI(videoUrl: string) {
  // Trigger the Visual Reverse Engineering engine
  const job = await replay.jobs.create({
    source: videoUrl,
    outputFormat: 'react-typescript',
    designSystem: 'tailwind-shadcn',
    extractTests: true
  });

  console.log(`Processing video... Job ID: ${job.id}`);

  // Wait for the AI agent to receive the structured UI map
  const result = await job.waitForCompletion();
  
  return result.components; // Returns production-ready React components
}

Once the API processes the video, it doesn't just return a single file. It returns a structured library. This is the only way agents build productionready from complex enterprise software like SAP, Oracle, or legacy COBOL-backed web portals.

How Replay solves the "Legacy Rewrite" failure#

Industry experts recommend a "Strangler Fig" pattern for modernization—gradually replacing pieces of a legacy system until the old system is gone. Replay accelerates this by providing the "Blueprint" for the new system based on the old one's behavior.

Visual Reverse Engineering is the methodology pioneered by Replay to reconstruct application logic and UI structures from video context. This removes the "blank page" problem for AI agents. Instead of telling an agent to "build a dashboard," you show it a 30-second clip of the legacy dashboard.

Example: Generated Production React Code#

When agents build productionready from Replay's extracted data, the code follows modern best practices, including TypeScript types and accessible ARIA labels.

tsx
import React, { useState } from 'react';
import { Button } from '@/components/ui/button';
import { Input } from '@/components/ui/input';

/**
 * Component extracted via Replay Visual Reverse Engineering
 * Source: Legacy CRM Dashboard Recording (00:12 - 00:45)
 */
export const CustomerSearch: React.FC = () => {
  const [query, setQuery] = useState('');

  return (
    <div className="p-6 bg-white rounded-lg shadow-sm border border-slate-200">
      <h3 className="text-lg font-semibold text-slate-900 mb-4">
        Search Customer Records
      </h3>
      <div className="flex gap-2">
        <Input 
          placeholder="Enter name or ID..." 
          value={query}
          onChange={(e) => setQuery(e.target.value)}
          className="max-w-md"
        />
        <Button variant="primary" onClick={() => console.log('Searching:', query)}>
          Execute Search
        </Button>
      </div>
    </div>
  );
};

The Flow Map: Beyond Single Components#

One of the biggest hurdles in UI modernization is navigation. How do pages link together? Replay's Flow Map feature uses temporal context to detect multi-page navigation. If your screen recording shows a user clicking a "Settings" link and moving to a new page, Replay maps that relationship.

This allows agents to build productionready from entire user journeys, not just isolated buttons. It automatically generates Playwright or Cypress E2E tests based on those recorded interactions, ensuring the new code behaves exactly like the old code. For more on this, read our guide on Automated E2E Test Generation.

Why Security-Conscious Teams Choose Replay#

Modernizing legacy systems often involves sensitive data (HIPAA, SOC2). Replay is built for regulated environments. You can run Replay on-premise or in a private cloud, ensuring that your screen recordings—and the code generated from them—never leave your secure perimeter.

This security is why enterprise architects trust Replay when they have agents build productionready from internal tools that contain PII or trade secrets. Learn more about our SOC2 compliance.

Frequently Asked Questions#

Can AI agents build productionready from low-quality video recordings?#

While Replay's engine is highly resilient, higher-resolution recordings (1080p+) yield better results. The AI uses sub-pixel analysis to determine spacing and typography. If the video is blurry, the agent might require more manual intervention to fix brand tokens, though Replay's Figma Sync can often override these visual discrepancies by pulling data directly from your design files.

Does Replay support frameworks other than React?#

Currently, Replay is optimized for the React ecosystem (Next.js, Vite, Tailwind, Shadcn). However, the Headless API provides raw JSON data and design tokens that can be used by AI agents to generate Vue, Svelte, or even raw HTML/CSS. Most developers find that having agents build productionready from the React output provides the cleanest path to modernization.

How does Replay handle complex logic like data fetching?#

Replay excels at the "Visual Logic"—the UI state, transitions, and client-side interactions. For backend data fetching, Replay identifies where data is being displayed and creates mock hooks or service layers. The AI agent can then be instructed to connect these hooks to your actual API endpoints.

Can I use Replay with Devin or OpenHands?#

Yes. Replay's Headless API is designed specifically for agentic workflows. By providing an agent with access to the Replay API, you give it "eyes." This allows the agent to see the UI it needs to build, drastically reducing the number of iterations required to reach a production-ready state.

How much faster is Replay compared to manual coding?#

According to Replay's analysis, teams see a 10x improvement in velocity. A project that would typically take 10 weeks of manual front-end development can be completed in 1 week when agents build productionready from screen recordings processed through Replay.

Ready to ship faster? Try Replay free — from video to production code in minutes.