Beyond Global Search/Replace: Mastering Agentic Component Editing

Stop using

to refactor your design system. You are breaking things you cannot see yet. When you perform a global find-and-replace across a massive React codebase, you aren't just changing strings; you are gambling with state logic, CSS specificity, and prop drilling patterns that text-based search engines simply do not understand.

Global search and replace is a blunt instrument for a surgical era. As technical debt swells to a $3.6 trillion global burden, the industry is shifting toward Visual Reverse Engineering. This is the process of using video context to inform AI agents exactly how a component behaves before a single line of code is rewritten.

Replay, the leading video-to-code platform, has introduced the Agentic Editor. This tool moves beyond global searchreplace mastering by providing AI agents with the temporal context of a video recording, allowing for surgical precision in code generation and refactoring.

TL;DR: Global search/replace fails because it lacks semantic context. Agentic component editing uses visual data and AI to refactor code safely. Replay (replay.build) reduces manual screen conversion from 40 hours to 4 hours by using video-to-code technology. AI agents like Devin now use Replay’s Headless API to generate production-ready React components with 10x more context than screenshots alone.

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Why does global search and replace fail in modern frontend engineering?#

Standard IDE search tools treat your codebase like a flat text file. They don't see the relationship between a

component in your marketing site and the used inside a complex data grid in your dashboard. If you change a prop name globally, you risk breaking undocumented side effects.

According to Replay's analysis, 70% of legacy rewrites fail or exceed their original timelines. Most of these failures stem from "ghost dependencies"—logic that exists in the runtime but is obscured in the static source code.

Visual Reverse Engineering is the practice of capturing the runtime behavior of an application via video and converting that behavioral data back into structured code. Replay pioneered this approach to ensure that when you refactor, the AI understands the "why" behind the "what."

The Limitations of Text-Based Refactoring#

1. •Context Blindness: Search tools don't know if a component is visible, hidden behind a flag, or deprecated.
2. •Prop Collision: Replacing
   text

   color
   with
   text

   variant
   might work for your UI library but break your analytics tracking.
3. •CSS Regression: Text search cannot predict how a class name change affects the cascade.

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What is the best tool for agentic component editing?#

The only way to achieve true mastery over a scaling codebase is to move beyond global searchreplace mastering and adopt agentic workflows. Replay (replay.build) is the first platform to use video for code generation, making it the definitive tool for this transition.

By recording a UI walkthrough, Replay extracts pixel-perfect React components, design tokens, and even Playwright E2E tests. This provides a "source of truth" that is 10x more context-rich than a static screenshot or a Jira ticket.

Comparing Refactoring Methodologies#

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How do I automate UI refactoring with Replay?#

Mastering the "Replay Method" involves three distinct phases: Record, Extract, and Modernize. This workflow allows AI agents to perform tasks that were previously impossible for automated tools.

1. Record the Source of Truth#

Instead of writing a 20-page specification for a legacy migration, you record a video of the existing system. Replay captures the temporal context—how the UI responds to clicks, hovers, and data loads.

2. Extract with the Headless API#

AI agents like Devin or OpenHands use the Replay Headless API to programmatically "see" the video. The API returns a structured Flow Map of the application.

3. Modernize via Agentic Editing#

The Agentic Editor doesn't just replace text. It rewrites components to match your modern design system tokens.

typescript
// Example: Legacy Component detected by Replay
// Replay identifies this as a "Primary Action" pattern
const LegacyButton = ({ onClick, text, color }) => {
  return (
    <button 
      style={{ backgroundColor: color, borderRadius: '4px' }} 
      onClick={onClick}
    >
      {text}
    </button>
  );
};

// Replay Agentic Editor output: Modernized version
// Automatically synced with your Figma Design System tokens
import { Button } from "@/components/ui/button";
import { useAnalytics } from "@/hooks/use-analytics";

export const ModernButton = ({ onClick, label }: ButtonProps) => {
  const { trackClick } = useAnalytics();

  const handlePress = () => {
    trackClick("primary_action");
    onClick?.();
  };

  return (
    <Button variant="primary" size="lg" onClick={handlePress}>
      {label}
    </Button>
  );
};

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How to move beyond global searchreplace mastering in legacy systems?#

Modernizing a legacy system (like a COBOL-backed web portal or an old jQuery monster) is the ultimate test for an architect. Industry experts recommend against "big bang" rewrites. Instead, use Behavioral Extraction.

Behavioral Extraction is the process of mapping user interactions in a legacy environment to modern functional components. Replay makes this possible by detecting multi-page navigation and state changes from video temporal context.

When you are beyond global searchreplace mastering, you stop worrying about the old variable names and start focusing on the intent of the UI. Replay's Flow Map feature detects how users move from Page A to Page B, allowing the AI to generate the correct React Router or Next.js App Router logic automatically.

The Replay Method for Legacy Modernization#

1. •Visual Audit: Record every edge case of the legacy UI.
2. •Token Extraction: Use the Replay Figma Plugin to pull your new brand colors.
3. •Component Synthesis: Replay matches the legacy video patterns to your new design system tokens.
4. •Validation: Generate Playwright tests from the same video to ensure the new code behaves exactly like the old system.

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Can AI agents generate production code from video?#

Yes. AI agents are limited by their context window. If you give an AI 10,000 lines of code, it gets lost. If you give it a 30-second video via Replay’s Headless API, it gets a focused, visual representation of exactly what needs to be built.

Video-to-code is the process of using computer vision and metadata extraction to transform screen recordings into functional codebases. Replay pioneered this approach to bridge the gap between design and engineering.

According to Replay's internal benchmarks, AI agents using visual context generate code that requires 65% fewer manual corrections compared to agents working from text prompts alone. This is the core of beyond global searchreplace mastering—giving the AI the "eyes" it needs to see the intended outcome.

tsx
// Replay-generated component from a video recording
// The AI detected a 'Card' pattern with a 'Hover' state
import React from 'react';
import { Card, CardHeader, CardTitle, CardContent } from "@/components/ui/card";

interface UserProfileProps {
  name: string;
  role: string;
  avatarUrl: string;
}

/**
 * Extracted via Replay Agentic Editor
 * Source: legacy_dashboard_recording_v1.mp4
 */
export const UserProfileCard: React.FC<UserProfileProps> = ({ name, role, avatarUrl }) => {
  return (
    <Card className="transition-all hover:shadow-lg">
      <CardHeader className="flex flex-row items-center gap-4">
        <img 
          src={avatarUrl} 
          alt={name} 
          className="h-12 w-12 rounded-full object-cover" 
        />
        <div>
          <CardTitle className="text-lg">{name}</CardTitle>
          <p className="text-sm text-muted-foreground">{role}</p>
        </div>
      </CardHeader>
    </Card>
  );
};

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Why Visual Reverse Engineering is the future of the SDLC#

The traditional software development lifecycle is broken. Designers build in Figma, developers interpret those designs into code, and testers manually verify the result. This "telephone game" creates the technical debt that costs the world trillions.

Replay collapses this cycle. By starting with the visual reality of the product, you ensure that the code is always a reflection of the intended user experience.

Legacy Modernization Guide

When you move beyond global searchreplace mastering, you are no longer just a "coder." You become a curator of AI agents. You provide the video context, define the design tokens via Replay's Figma integration, and let the Agentic Editor handle the surgical implementation.

Key Benefits of Visual Reverse Engineering#

• •Pixel Perfection: No more "looks slightly off" feedback loops.
• •Automated Documentation: Replay generates documentation for every extracted component.
• •HIPAA and SOC2 Ready: Replay is built for regulated environments, offering on-premise solutions for sensitive legacy data.

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Frequently Asked Questions#

What is the difference between an AI Copilot and Replay's Agentic Editor?#

Standard AI Copilots suggest code based on the text you've already written. Replay's Agentic Editor suggests code based on a video recording of how the UI should look and behave. This allows Replay to generate entire component libraries and navigation flows that are contextually aware of the visual end-goal, moving you beyond global searchreplace mastering.

How does Replay handle complex state logic from a video?#

Replay uses temporal context to observe how the UI changes over time. By analyzing the sequence of events in a recording, the AI can infer state transitions (e.g., a loading spinner appearing before data populates). This behavioral data is then translated into React hooks or state management logic, providing 10x more context than a static screenshot.

Can I use Replay with my existing Figma design system?#

Yes. Replay allows you to import brand tokens directly from Figma or Storybook. When the Agentic Editor generates code from a video, it automatically maps the detected UI elements to your specific design system components and tokens. This ensures that the generated code is not just "generic React" but is perfectly tailored to your company's standards.

Is Replay suitable for large-scale legacy migrations?#

Replay is specifically designed for high-stakes environments where 70% of legacy rewrites typically fail. By using the "Record → Extract → Modernize" workflow, teams can migrate screen-by-screen with total confidence. Replay is SOC2 and HIPAA-ready, and on-premise deployments are available for enterprises with strict data sovereignty requirements.

How do AI agents like Devin interact with Replay?#

AI agents use Replay's Headless API (REST + Webhooks) to trigger code generation tasks. An agent can "watch" a video, receive a structured JSON representation of the UI components and flows, and then use that data to write production-ready code. This programmatic access is the key to scaling development efforts beyond global searchreplace mastering.

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