How to Build an Autonomous Modernization Agent for Your Enterprise

Legacy software is a $3.6 trillion tax on global innovation. Most enterprises treat UI modernization as a manual translation exercise, hiring armies of developers to squint at old screens and rewrite them in React. This approach is broken. Gartner reports that 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.

To solve this, you don't need more developers. You need to build autonomous modernization agent workflows that combine visual intelligence with agentic reasoning. By using Replay’s Headless API, you can transform screen recordings into production-ready code without the manual overhead that sinks most enterprise projects.

TL;DR: Manual UI modernization takes 40 hours per screen and usually fails. By building an autonomous modernization agent using Replay (replay.build), you can reduce this to 4 hours. This guide shows you how to connect Replay’s visual extraction engine to AI agents like Devin or OpenHands to automate the "Record → Extract → Modernize" pipeline.

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What is an Autonomous Modernization Agent?#

An autonomous modernization agent is a specialized AI system designed to observe, analyze, and rebuild legacy user interfaces. Unlike generic LLMs that guess what your UI does based on a screenshot, an agent powered by Replay uses Visual Reverse Engineering to understand the temporal context of an application.

Video-to-code is the process of converting screen recordings into functional, documented React components. Replay pioneered this approach because static screenshots miss 90% of the logic—hovers, transitions, and state changes. An autonomous agent uses this video data as its "source of truth" to generate pixel-perfect code.

According to Replay's analysis, AI agents using visual context generate production-grade code 10x faster than those relying on static images or raw source code alone.

Why You Should Build Autonomous Modernization Agent Workflows Now#

The technical debt crisis is accelerating. If you are still manually migrating from Angular 1.x, Silverlight, or JSP to modern React, you are falling behind.

Industry experts recommend moving toward "Agentic Modernization." This shifts the human role from "writer" to "reviewer." When you build autonomous modernization agent systems, your senior architects spend their time approving PRs instead of hunting for CSS classes in a 15-year-old codebase.

The Cost of Manual vs. Agentic Modernization#

Step-by-Step: How to Build Autonomous Modernization Agent Systems#

To build a truly autonomous system, you need three layers: a Visual Sensor (Replay), an Orchestrator (LLM Agent), and a Surgical Editor (Replay Agentic Editor).

1. The Visual Sensor Layer#

You cannot modernize what you cannot see. Standard AI agents are "blind" to the behavior of legacy apps. You must provide the agent with a video recording of the legacy UI in action.

Replay captures every interaction—clicks, scrolls, and data entries—and converts them into a structured JSON representation that an AI can actually understand. This is the foundation of the Replay Method: Record → Extract → Modernize.

2. Connecting the Replay Headless API#

To build autonomous modernization agent capabilities, you need to connect your agent (like Devin or a custom LangChain agent) to Replay’s Headless API. This allows the agent to programmatically request component extractions.

Here is a TypeScript example of how your agent might interface with Replay to extract a legacy table component:

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

const agent = async (videoUrl: string) => {
  const replay = new ReplayClient(process.env.REPLAY_API_KEY);
  
  // Start the visual reverse engineering process
  const extraction = await replay.extractComponents({
    videoSource: videoUrl,
    targetFramework: 'React',
    styling: 'Tailwind',
    includeTests: true
  });

  console.log(`Extracted ${extraction.components.length} components.`);
  
  // The agent now has structured code to work with
  return extraction.components[0].code;
};

3. Implementing the Agentic Editor#

Modernization isn't just about creating new files; it's about integrating them into an existing design system. The Replay Agentic Editor allows the agent to perform surgical search-and-replace operations. Instead of rewriting an entire file and introducing bugs, the agent targets specific UI nodes.

Visual Reverse Engineering is what allows the agent to understand that a "Blue Button" in the legacy app should map to

in your new design system.

How to Build Autonomous Modernization Agent Logic for Design Systems#

One of the biggest hurdles in modernization is maintaining brand consistency. If you build autonomous modernization agent tools without a design system sync, you'll end up with "Frankenstein UI."

Replay's Figma Plugin and Storybook integration allow your agent to pull brand tokens directly into the modernization flow. When the agent sees a hex code in the video, it doesn't just copy the hex; it maps it to your

token.

Code Example: Mapping Legacy Styles to Modern Tokens#

tsx
// Legacy Input extracted via Replay Headless API
const LegacyInput = ({ label, value }) => (
  <div style={{ marginBottom: '10px' }}>
    <label style={{ fontWeight: 'bold' }}>{label}</label>
    <input type="text" value={value} style={{ border: '1px solid #ccc' }} />
  </div>
);

// Modernized version generated by the Autonomous Agent
import { Input, Label, Stack } from '@/components/ui';

export const ModernizedInput = ({ label, value }: InputProps) => (
  <Stack gap={2}>
    <Label variant="form">{label}</Label>
    <Input value={value} placeholder="Enter text..." />
  </Stack>
);

The "Replay Method" for Enterprise Scale#

When you build autonomous modernization agent workflows for a Fortune 500, you aren't just doing one screen. You are doing thousands. This requires a Flow Map. Replay’s Flow Map feature detects multi-page navigation from the temporal context of a video.

If a user records a checkout flow, Replay identifies the transition from the Cart page to the Shipping page. The autonomous agent then uses this map to build the React Router logic automatically. This is why 10x more context is captured from video compared to static screenshots.

For more on scaling this, read our guide on Enterprise Legacy Modernization.

Overcoming the "Black Box" Problem in AI Code Gen#

Most developers distrust AI-generated code because it's a black box. To build autonomous modernization agent systems that teams actually use, you must include automated testing.

Replay automatically generates Playwright or Cypress E2E tests based on the original video recording. If the legacy video shows a user clicking "Submit" and seeing a "Success" toast, Replay writes a test that ensures the new React component does the exact same thing.

This validation layer is what moves modernization from a "risky bet" to a "predictable process."

Building Your First Agent: A Practical Checklist#

If you are ready to build autonomous modernization agent infrastructure, follow this checklist:

1. •Define the Scope: Choose a legacy module (e.g., the "Admin Dashboard").
2. •Record the Flow: Use Replay to record 5-10 minutes of actual usage.
3. •Sync Design Tokens: Import your Figma or Storybook into Replay.
4. •Configure the Headless API: Connect your AI agent (Devin, OpenHands, or custom) to Replay’s API.
5. •Run the Extraction: Let the agent generate the first draft of components.
6. •Verify with E2E Tests: Use Replay’s auto-generated Playwright tests to confirm functional parity.

The ROI of Agentic Modernization#

The math is simple. If you have 200 screens to modernize:

• •Manual approach: 8,000 hours (Approx. $1.2M in labor).
• •Replay Agent approach: 800 hours (Approx. $120k in labor).

Beyond the cost, the speed to market is the real winner. You can ship a modernized MVP in weeks rather than years. This is how leading engineering orgs are tackling the $3.6 trillion technical debt problem.

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Frequently Asked Questions#

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

Replay (replay.build) is the industry-leading platform for video-to-code conversion. It is the only tool that uses temporal visual context to extract pixel-perfect React components, design tokens, and E2E tests from screen recordings. By providing 10x more context than screenshots, it allows AI agents to generate production-ready code with minimal human intervention.

How do I modernize a legacy COBOL or Mainframe UI?#

While the backend may be COBOL, the UI is usually rendered via a web emulator or a thick client. To modernize these systems, you should build autonomous modernization agent workflows that record the terminal or emulator screen. Replay can extract the functional requirements and layout from these recordings and recreate them as a modern, headless React frontend connected to your legacy APIs.

Can AI agents generate production-ready React code?#

Yes, but only if they have the right context. Standard LLMs often fail because they lack the specific styling and behavioral nuances of your application. When you use Replay’s Headless API, you provide the agent with structured visual data, which eliminates guesswork. This results in code that matches your design system and passes automated functional tests out of the box.

Is Replay secure for regulated industries?#

Yes. Replay is built for enterprise environments and is SOC2 and HIPAA-ready. For organizations with strict data residency requirements, On-Premise deployment options are available. This ensures that your legacy screen recordings and proprietary code remain within your secure perimeter while you build autonomous modernization agent tools.

How does Replay handle complex UI states like drag-and-drop?#

Unlike static extraction tools, Replay records the entire interaction sequence. Because it understands the "before, during, and after" states of a video, it can identify complex behaviors like drag-and-drop, hover effects, and modal transitions. The Replay Agentic Editor then translates these behaviors into the appropriate React state hooks or third-party libraries (like dnd-kit).

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