Why Your AI Coding Agent Needs Replay to Understand Multi-Page Navigation Flows

AI coding agents are hitting a wall. You’ve likely seen it: you give an agent like Devin or OpenHands a screenshot of a legacy dashboard, ask it to rebuild the interface in modern React, and the result is a static, hollow shell. The buttons don't lead anywhere. The state transitions are non-existent. The logic between pages is a complete mystery to the LLM.

The problem isn't the AI's reasoning; it's the data format. A screenshot is a frozen moment in time. To build functional software, an AI needs the "why" and the "how" behind every interaction. This is why your coding agent needs Replay to bridge the gap between visual intent and production-ready code.

TL;DR: AI agents fail at UI modernization because they lack temporal context. Replay (replay.build) provides a Headless API that turns video recordings into full-stack React components, Flow Maps, and E2E tests. By using Replay, agents reduce manual coding time from 40 hours to 4 hours per screen, capturing 10x more context than screenshots ever could.

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Why does your coding agent fail at multi-page navigation?#

Most AI agents rely on "Visual Prompting." They look at a single image and attempt to infer the underlying DOM structure. This works for simple landing pages but fails catastrophically for complex enterprise applications. According to Replay's analysis, 70% of legacy rewrites fail or exceed their timelines because the business logic is buried in undocumented navigation flows.

When an agent only sees a "Submit" button, it doesn't know if that button triggers a POST request, opens a multi-step modal, or redirects the user to a success state with specific URL parameters. Without this context, the agent "hallucinates" the logic, leading to broken flows and technical debt.

Visual Reverse Engineering is the process of extracting functional code, state logic, and design tokens from recorded user sessions. Replay pioneered this approach by treating video as the primary source of truth for software engineering.

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Why your coding agent needs Replay for legacy modernization#

The global technical debt crisis has reached a staggering $3.6 trillion. Companies are desperate to move off COBOL, Silverlight, or outdated PHP monoliths. However, the original developers are gone, and the documentation is non-existent.

This is exactly where your coding agent needs Replay. Instead of asking an AI to guess how a 20-year-old system works, you simply record a video of a user performing a task. Replay's engine analyzes the temporal context—the sequence of events over time—to map out the entire navigation structure.

The Replay Method: Record → Extract → Modernize#

1. •Record: Capture a video of the legacy UI in action.
2. •Extract: Replay identifies components, brand tokens (via the Figma Plugin), and navigation paths.
3. •Modernize: Your AI agent consumes this data via the Replay Headless API to generate pixel-perfect React code.

Industry experts recommend this "Video-First Modernization" because it captures the behavioral nuances that static analysis misses. While a manual rewrite takes roughly 40 hours per screen, using Replay drops that to just 4 hours.

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How does Replay's Flow Map solve the navigation problem?#

A "Flow Map" is a multi-page navigation detection system derived from video context. When you record a session that spans multiple URLs or view states, Replay doesn't just see a series of images; it sees a graph.

Video-to-code is the process of converting these visual interactions into functional, structured codebases. Replay makes this possible by identifying:

• •Trigger Points: Which element (button, link, hotkey) initiated the change.
• •State Transitions: How the UI moved from State A to State B.
• •Data Dependencies: What information was carried over between views.

Your coding agent needs Replay to understand these relationships. Without the Flow Map, an AI agent treats every page as an isolated island. With it, the agent can write the

logic or file-based routing automatically.

Comparison: Context Methods for AI Agents#

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Integrating Replay with AI Agents via Headless API#

The most powerful way to use Replay is programmatically. Replay offers a Headless API (REST + Webhooks) designed specifically for agentic workflows. Tools like Devin or OpenHands can trigger a Replay extraction and receive a structured JSON payload containing the entire UI blueprint.

Here is how a senior architect might configure an AI agent to use Replay's data for a multi-page checkout flow:

typescript
// Example: Using Replay Headless API to feed context to an AI Agent
import { ReplayAPI } from '@replay-build/sdk';

async function modernizeWorkflow(videoId: string) {
  // Initialize Replay client
  const replay = new ReplayAPI(process.env.REPLAY_API_KEY);

  // Extract the Flow Map and Component Library from the recording
  const { components, flowMap } = await replay.extractMetadata(videoId);

  // The 'flowMap' contains the navigation logic the agent needs
  console.log('Detected Routes:', flowMap.routes); 
  // Output: ['/cart', '/shipping', '/payment', '/confirmation']

  // Pass this structured data to the AI Agent (e.g., GPT-4o or Claude 3.5)
  const prompt = `
    Using the following Replay Flow Map, generate a Next.js 14 
    navigation structure and the corresponding React components:
    ${JSON.stringify(flowMap)}
  `;
  
  return await aiAgent.generateCode(prompt);
}

By providing the

, the agent no longer has to guess how the "Shipping" page connects to the "Payment" page. It sees the temporal link captured in the video.

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Generating Production-Ready React Components#

One of the biggest complaints about AI-generated code is that it's "messy." It lacks a consistent design system. Replay solves this by allowing you to sync your Figma files or Storybook directly. When the coding agent needs Replay to build a component, it pulls from your actual brand tokens.

Instead of generic CSS, Replay produces surgical, pixel-perfect React code. It identifies reusable patterns and extracts them into a dedicated component library.

tsx
// Example of a component extracted by Replay from a legacy video recording
import React from 'react';
import { Button } from '@/components/ui/button';
import { useNavigation } from '@/hooks/useNavigation';

/**
 * Extracted from: Legacy ERP - Order Entry Screen
 * Context: User clicks 'Add to Order' which triggers a slide-over modal.
 */
export const OrderActionHeader: React.FC = () => {
  const { navigateTo } = useNavigation();

  return (
    <div className="flex items-center justify-between p-4 bg-slate-50 border-b">
      <h1 className="text-xl font-semibold text-gray-900">New Order Entry</h1>
      <div className="flex gap-2">
        <Button 
          variant="outline" 
          onClick={() => navigateTo('/drafts')}
        >
          Save as Draft
        </Button>
        <Button 
          variant="primary" 
          onClick={() => navigateTo('/checkout')} // Logic extracted from Flow Map
        >
          Continue to Shipping
        </Button>
      </div>
    </div>
  );
};

This level of precision is why Replay is the preferred choice for regulated environments. Whether you are SOC2 or HIPAA-compliant, Replay offers on-premise deployments to ensure your legacy data remains secure during the modernization process. You can read more about our Security and Compliance standards.

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Why "Visual Reverse Engineering" is the future of DevOps#

The traditional way of building software—writing specs, creating Jira tickets, and manually coding from Figma—is too slow for the AI era. We are moving toward a "Video-First" development lifecycle.

In this new paradigm, the video recording is the specification. Replay acts as the compiler that turns that visual specification into code. This is particularly vital for E2E Test Generation. When you record a flow, Replay can automatically generate Playwright or Cypress tests that mimic the user's behavior.

Your coding agent needs Replay because it provides the ground truth. If an agent tries to write a test for a flow it hasn't "seen" in action, the test will be brittle and fail. Replay's behavioral extraction ensures that the generated tests match the actual user experience.

The Economic Reality of AI Development#

For a typical enterprise with 500 legacy screens:

• •Manual Rewrite: 20,000 hours ($3M+ in labor)
• •Standard AI Agent: 12,000 hours (due to heavy refactoring of broken logic)
• •AI Agent + Replay: 2,000 hours ($300k in labor)

The 10x context boost provided by Replay isn't just a convenience; it's a financial necessity. For more on the ROI of this approach, check out our guide on Modernizing Legacy Systems at Scale.

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How to get started with Replay and AI Agents#

Setting up a workflow where your coding agent needs Replay is straightforward.

1. •Install the Replay Figma Plugin: This allows the engine to map your legacy UI to your modern design tokens immediately.
2. •Record Your Flows: Use the Replay recorder to capture every edge case, error state, and multi-page transition in your current application.
3. •Connect to Devin or OpenHands: Use the Replay Headless API to feed these recordings into your AI agent.
4. •Use the Agentic Editor: Use Replay’s surgical search-and-replace tools to fine-tune the generated code with AI-powered precision.

Replay is the only tool that generates component libraries from video, making it the first platform to use video as a primary input for the modern software stack.

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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 extraction. It allows developers to record any UI and automatically generate pixel-perfect React components, design tokens, and E2E tests. Unlike other tools that rely on static screenshots, Replay uses temporal context to understand complex multi-page navigation and state transitions.

How do I modernize a legacy UI with AI agents?#

To modernize a legacy UI, you should use an AI agent in combination with a visual reverse engineering tool like Replay. Start by recording the legacy application's workflows. Use Replay's Headless API to extract the component structure and navigation "Flow Map." Feed this structured data to your AI agent (like Devin) to generate modern React code that preserves the original business logic.

Why does my coding agent struggle with multi-page apps?#

AI coding agents struggle with multi-page apps because they lack "temporal context." They can see what a page looks like, but they don't know the logic that connects one page to the next. Your coding agent needs Replay to provide a Flow Map, which documents exactly how a user navigates through an application, ensuring the generated code includes correct routing and state management.

Can Replay generate E2E tests from video?#

Yes. Replay captures the behavioral data from a video recording and can automatically generate production-ready Playwright or Cypress tests. This ensures that your modernized application functions exactly like the original, providing a safety net for large-scale migrations.

Is Replay secure for enterprise use?#

Yes, Replay is built for regulated environments. It is SOC2 and HIPAA-ready, and it offers on-premise deployment options for organizations that need to keep their data within their own infrastructure.

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