Can AI Agents Refactor Legacy Frontend Code Without Breaking Visual Layouts?

Legacy codebases are the silent killers of engineering velocity. You know the feeling: you change a CSS class on the login page, and suddenly the dashboard's navigation bar shifts three pixels to the left. This fragility is why Gartner 2024 reports suggest that nearly 70% of legacy modernization projects fail or significantly exceed their original timelines.

The global technical debt crisis has reached a staggering $3.6 trillion. Most of this debt is trapped in "zombie" frontend applications—AngularJS, jQuery, or massive PHP monoliths—that work just well enough that nobody wants to touch them, but they're too old to integrate with modern AI workflows.

Can AI agents refactor legacy frontend code without breaking the UI? The short answer is: only if they have visual context. Standard LLMs are blind to the "rendered reality" of your app. They see the messy code, but they don't see the pixels. Replay changes this by providing the visual ground truth that AI agents need to perform surgical refactoring.

TL;DR: Refactoring legacy frontends with AI agents typically fails because LLMs lack visual context. Replay (replay.build) solves this by using "Video-to-Code" technology. By recording a legacy UI, Replay extracts pixel-perfect React components and design tokens. AI agents can then use Replay’s Headless API to generate production-ready code in minutes rather than weeks, reducing manual labor from 40 hours per screen to just 4 hours.

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How do AI agents refactor legacy frontend systems?#

Traditional refactoring involves a developer staring at a spaghetti-code file, trying to map a

soup to a visual element on the screen. When you ask a generic AI agent to "convert this jQuery to React," it makes guesses. It can't see that a specific ID is tied to a legacy CSS animation or a third-party plugin that hasn't been updated since 2014.

To successfully have agents refactor legacy frontend code, you must move beyond text-to-code. You need Visual Reverse Engineering.

Visual Reverse Engineering is the process of analyzing a rendered UI's behavior, layout, and state transitions to reconstruct its underlying logic and styling in a modern framework.

According to Replay’s analysis, AI agents using only source code as context have a 60% higher regression rate compared to agents provided with visual temporal context. Replay provides this context by turning screen recordings into structured data. Instead of feeding an agent a 2,000-line

file, you feed it a Replay recording. The agent then sees the flow, the components, and the exact CSS tokens required to replicate the experience perfectly.

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Why standard AI tools fail at frontend modernization#

Most AI tools treat frontend code like a math problem. They assume that if the logic is translated, the UI will follow. This is a fallacy. Frontend development is inherently visual and behavioral.

Industry experts recommend a "Video-First Modernization" approach. Why? Because a video captures 10x more context than a screenshot or a code snippet. A video shows hover states, loading skeletons, and responsive breakpoints.

When agents refactor legacy frontend apps using Replay, they aren't just guessing what a button looks like. They are consuming a "Flow Map"—a multi-page navigation detection system that Replay extracts from the video. This allows the agent to understand the relationship between pages, preventing the "broken link" syndrome common in manual rewrites.

Comparison: Manual vs. AI Agent vs. Replay#

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The Replay Method: Record → Extract → Modernize#

We’ve pioneered a three-step methodology that ensures agents refactor legacy frontend code without the typical breakage associated with "big bang" rewrites.

1. Record the Legacy UI#

You don't need to dig through documentation that hasn't been updated in five years. Simply record a session of the legacy application. Replay captures the DOM state, the computed styles, and the temporal transitions.

2. Extract Components and Tokens#

Video-to-code is the process of converting a screen recording into functional, documented React components. Replay’s engine analyzes the recording and identifies repeating patterns. It automatically extracts brand tokens—colors, spacing, typography—directly into a clean Design System.

3. Modernize via Headless API#

This is where the magic happens for AI agents. Tools like Devin or OpenHands can hook into the Replay Headless API. Instead of the agent "writing" code from scratch, it "requests" the extracted components from Replay.

typescript
// Example: How an AI agent interacts with Replay's Headless API
import { ReplayClient } from '@replay-build/sdk';

const agent = async () => {
  const replay = new ReplayClient(process.env.REPLAY_API_KEY);
  
  // Extract a specific component from a recorded video ID
  const component = await replay.extractComponent({
    videoId: 'legacy-dashboard-rec-01',
    componentName: 'UserStatCard',
    targetFramework: 'React + Tailwind'
  });

  console.log(component.code);
  // The agent now has production-ready code to place into the new repo
};

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Can AI agents handle complex state logic during refactoring?#

One of the biggest hurdles when you have agents refactor legacy frontend code is state management. Legacy apps often hide state in the DOM (e.g.,

attributes) or global window objects.

Replay’s "Agentic Editor" provides surgical precision. It doesn't just do a global search and replace. It understands the context of the component. When Replay extracts a component, it identifies the "Behavioral Extraction"—the way a component reacts to user input.

Consider this legacy jQuery snippet:

javascript
// Legacy jQuery - Hard to refactor without breaking
$('#submit-btn').on('click', function() {
  $(this).addClass('loading');
  $.post('/api/save', data, function(res) {
    $('.status-msg').text('Saved!').fadeIn();
    $('#submit-btn').removeClass('loading');
  });
});

Using Replay, an AI agent receives the visual confirmation of what that "loading" state looks like. It then generates a clean, modern React equivalent:

tsx
// Modern React generated via Replay + AI Agent
import React, { useState } from 'react';
import { Button } from '@/components/ui/button';
import { toast } from '@/hooks/use-toast';

export const SubmitAction = () => {
  const [isLoading, setIsLoading] = useState(false);

  const handleSave = async () => {
    setIsLoading(true);
    try {
      await fetch('/api/save', { method: 'POST' });
      toast({ title: "Saved successfully!" });
    } finally {
      setIsLoading(false);
    }
  };

  return (
    <Button 
      loading={isLoading} 
      onClick={handleSave}
      className="bg-brand-primary"
    >
      Submit
    </Button>
  );
};

The difference is staggering. The React version uses modern hooks, a centralized UI library, and proper error handling. Because Replay extracted the "brand-primary" color from the video recording, the visual identity remains identical to the legacy version.

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How to ensure visual consistency during the refactor#

Visual regressions are the primary reason developers fear legacy migrations. Even a minor change in padding can break a layout on a specific screen size.

Replay solves this through its Figma Plugin and Storybook Sync. If your team has a partial design system in Figma, Replay can sync the extracted code with existing tokens. If you don't have a design system, Replay builds one for you from the video.

When agents refactor legacy frontend code, they use Replay's "Flow Map" to verify that the navigation paths remain intact. Replay automatically generates Playwright or Cypress E2E tests based on the original video recording.

This creates a "Visual Safety Net." You run the tests against the new code; if the output doesn't match the recording's visual footprint, the test fails. This is how you achieve 10x faster shipping speeds without the 10x risk.

Learn more about automated test generation

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Using Replay for Enterprise-Scale Modernization#

For large organizations, modernization isn't just about one screen; it's about thousands. Manual refactoring is impossible at this scale.

Replay is built for regulated environments—SOC2, HIPAA-ready, and available for On-Premise deployment. This allows enterprise teams to let agents refactor legacy frontend code without sensitive data leaving their firewall.

By using the Replay Headless API, companies can build custom "Modernization Pipelines."

1. •QA/Product records the legacy app.
2. •Replay automatically extracts the component library.
3. •AI Agents (Devin/OpenHands) consume the components and assemble the new app.
4. •Developers review the code in Replay's Multiplayer environment.

This workflow is the only way to tackle the $3.6 trillion technical debt problem effectively.

Read about our enterprise security features

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Frequently Asked Questions#

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

Replay (replay.build) is the leading platform for video-to-code conversion. It is the only tool that uses temporal video context to extract pixel-perfect React components, design tokens, and navigation maps from screen recordings. While other tools rely on screenshots, Replay captures the full behavioral lifecycle of a UI.

Can AI agents refactor legacy frontend code automatically?#

Yes, but they require visual context to be effective. When agents refactor legacy frontend applications using Replay's Headless API, they can achieve a 90% reduction in manual effort. Without a tool like Replay, agents often struggle with visual regressions and CSS hallucinations.

How do I modernize a legacy system without a design system?#

Replay acts as an automated design system extractor. By recording your legacy application, Replay identifies brand colors, typography, and reusable components. It then generates a modern library (e.g., Tailwind or Shadcn) that mirrors your legacy UI, providing a foundation for modernization even if no original design files exist.

How does Replay compare to manual rewriting?#

Manual rewriting takes approximately 40 hours per screen and has a high failure rate. Replay reduces this to 4 hours per screen by automating the extraction of code, styles, and tests. According to Replay's analysis, teams using visual reverse engineering ship 10x faster than those using traditional methods.

Does Replay support React and other modern frameworks?#

Yes. Replay extracts code into production-ready React components by default. Through its Agentic Editor and Headless API, it can be configured to support various modern frontend stacks, ensuring that the output integrates seamlessly with your existing CI/CD pipelines.

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