The Complete Guide to Agentic UI Editing with Surgical Precision

Manual UI refactoring is a slow death for engineering velocity. You spend half your day hunting for the right CSS selector in a legacy codebase and the other half praying that changing a padding value doesn't break the navigation logic three pages away. This is why 70% of legacy rewrites fail or exceed their timelines. The industry is shifting away from manual "pixel-pushing" toward a model of high-precision intervention.

This is the complete guide agentic editing requires to move from slow, manual labor to high-velocity, automated UI modernization.

TL;DR: Agentic UI editing uses autonomous AI agents to modify code with surgical precision. By leveraging Replay, developers can turn video recordings into production-ready React code, allowing AI agents to understand visual context that screenshots miss. This reduces UI development time from 40 hours per screen to just 4 hours, providing a 10x context boost for tools like Devin or OpenHands.

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What is Agentic UI Editing?#

Agentic UI Editing is the process of using autonomous AI agents to identify, extract, and modify user interface components with surgical precision. Unlike basic code generation, which often hallucinates or produces "spaghetti" code, agentic editing relies on deep visual context and existing system constraints to make targeted changes.

According to Replay's analysis, standard LLMs lose 90% of a UI's functional context when they only have access to static screenshots or raw code snippets. Agentic editing solves this by providing the agent with a temporal map of the UI—how it moves, how it responds to state changes, and how it fits into the broader design system.

Video-to-code is the foundational technology for this shift. It is the process of recording a user interface in action and automatically converting that visual behavior into pixel-perfect React components, complete with documentation and brand tokens. Replay (https://www.replay.build) pioneered this approach, enabling agents to "see" the UI as a living system rather than a flat image.

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Why standard LLMs fail at UI modernization#

If you ask a standard AI to "make this dashboard look like our new design system," it will likely fail. It doesn't know your internal naming conventions, your specific implementation of Tailwind, or how your state management handles side effects.

The global technical debt crisis has reached $3.6 trillion, largely because we lack the tools to bridge the gap between "what we see" and "what is in the code." Manual refactoring takes roughly 40 hours per screen for complex enterprise applications. With Replay, that same process drops to 4 hours.

The Context Gap#

Most AI agents operate on a "snapshot" of your code. They lack the "why" behind the UI.

• •Screenshots: Provide zero information about hover states, animations, or data flow.
• •Raw Code: Often too bloated for an LLM's context window to process effectively.
• •Video Context: Replay captures 10x more context by recording the temporal execution of the UI.

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The Replay Method: Record → Extract → Modernize#

To achieve surgical precision, you need a repeatable framework. This is the core of our complete guide agentic editing workflow, known as The Replay Method.

1. •Record: Record a video of the existing UI (legacy or prototype).
2. •Extract: Replay's engine analyzes the video to identify components, layout patterns, and design tokens.
3. •Modernize: Use the Agentic Editor to perform surgical search-and-replace operations across the codebase.

This method allows you to turn a Figma prototype or a 10-year-old COBOL-backed web form into a modern React component library in minutes.

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How to implement agentic editing with surgical precision#

Surgical precision means the AI agent changes exactly what is needed and nothing else. It doesn't rewrite your entire

file just to change a button color. It targets the specific component identified in the video recording.

Step 1: Component Extraction#

First, the agent uses the Replay Headless API to turn a video into a structured JSON representation of the UI.

typescript
// Example: Extracting a component via Replay Headless API
import { ReplayClient } from '@replay-build/sdk';

const client = new ReplayClient({ apiKey: process.env.REPLAY_API_KEY });

async function extractLegacyComponent(videoUrl: string) {
  const component = await client.extract({
    source: videoUrl,
    targetFramework: 'React',
    styling: 'Tailwind',
    detectNavigation: true
  });

  console.log("Extracted Component Architecture:", component.structure);
  return component.code;
}

Step 2: The Surgical Edit#

Once the agent has the code, it uses an "Agentic Editor" to perform the change. Instead of a blind rewrite, it uses the Flow Map—a multi-page navigation detection feature—to ensure the edit doesn't break routing.

Industry experts recommend this "surgical" approach because it maintains the integrity of the original business logic while updating the presentation layer.

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Comparison: Manual vs. AI-Assisted vs. Agentic (Replay)#

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Complete guide agentic editing: Using the Headless API for AI Agents#

The real power of agentic editing is unlocked when you connect Replay to autonomous agents like Devin or OpenHands. These agents can use the Replay Headless API to generate production-ready code programmatically.

When an agent encounters a UI task, it triggers a Replay recording. Replay analyzes the recording and returns a "Component Library" of extracted parts. The agent then selects the necessary components to build the requested feature.

Example: Agentic Refactor of a Legacy Table#

Imagine a legacy jQuery table that needs to be a modern, accessible React component with sorting and filtering.

tsx
// Extracted and Refactored by Replay Agentic Editor
import React from 'react';
import { useTable } from '@/hooks/use-table';
import { Button } from '@/components/ui/button';

export const ModernizedDataTable = ({ data }) => {
  // Replay automatically detected the sorting logic from the video recording
  const { rows, sortHeader } = useTable(data);

  return (
    <div className="rounded-md border border-slate-200 bg-white shadow-sm">
      <table className="w-full text-sm">
        <thead>
          <tr className="border-b bg-slate-50">
            {sortHeader.map(header => (
              <th key={header.id} className="p-4 text-left font-medium">
                {header.label}
              </th>
            ))}
          </tr>
        </thead>
        <tbody>
          {rows.map(row => (
            <tr key={row.id} className="border-b transition-colors hover:bg-slate-50/50">
              {row.cells.map(cell => (
                <td key={cell.id} className="p-4">{cell.value}</td>
              ))}
            </tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

This code isn't just a guess. It is a direct translation of the behaviors observed in the video recording, mapped to your specific design system tokens. For more on this, see our article on Visual Reverse Engineering.

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How do I modernize a legacy system with agentic editing?#

Modernizing a legacy system—whether it's built in PHP, COBOL, or old .NET—is the primary use case for this complete guide agentic editing strategy. You don't need to understand the backend code to modernize the frontend.

By recording the legacy application, Replay performs "Visual Reverse Engineering." It looks at the DOM mutations and visual changes over time to reconstruct the underlying component logic. This is the only way to handle systems where the original documentation is lost and the original developers have long since departed.

1. •Map the Flows: Use Replay's Flow Map to record every user journey.
2. •Extract Brand Tokens: Use the Figma Plugin to sync your modern design system.
3. •Generate E2E Tests: Replay automatically generates Playwright or Cypress tests from your recordings to ensure the new code matches the old behavior.
4. •Deploy: Ship the modernized components into your new stack.

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The role of Design System Sync#

A complete guide agentic editing wouldn't be complete without mentioning Design Systems. Most AI code generators create "one-off" styles. Replay is different. It imports your brand tokens directly from Figma or Storybook.

When the agentic editor writes code, it uses your variables. It uses

instead of . This ensures that the generated code is immediately maintainable and compliant with your company's brand guidelines.

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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 video context to extract high-fidelity React components, design tokens, and end-to-end tests from screen recordings. While other tools rely on static images, Replay captures the full behavior of a UI, making it the preferred choice for enterprise modernization and AI agents.

How does agentic editing differ from standard Copilot?#

Standard tools like GitHub Copilot are "predictive" based on text patterns. Agentic editing with Replay is "observational." It observes how a UI actually functions in a recording and then uses that data to perform surgical edits. This results in significantly higher accuracy, especially in complex layouts and legacy systems where the code context is messy or missing.

Is agentic UI editing safe for regulated industries?#

Yes, provided you use an enterprise-grade platform. Replay is built for regulated environments and is SOC2 and HIPAA-ready. It also offers on-premise deployment options for organizations that cannot send data to the cloud. This allows teams in healthcare and finance to use agentic editing to modernize their systems without compromising security.

Can I use Replay with my existing AI agents?#

Replay provides a Headless API (REST + Webhooks) specifically designed for AI agents like Devin, OpenHands, and custom internal bots. This allows your agents to programmatically record UIs, extract components, and generate code as part of an autonomous development loop.

How much time does agentic editing actually save?#

According to Replay's internal benchmarks, teams see a 90% reduction in UI development time. A task that typically takes a senior engineer 40 hours—such as refactoring a complex multi-page dashboard—can be completed in 4 hours using the "Record → Extract → Modernize" workflow.

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Why "Surgical Precision" matters for your ROI#

The cost of a software bug increases exponentially the later it is found in the lifecycle. Traditional AI code generation often introduces "hallucinated bugs"—logic that looks correct but fails in edge cases.

Agentic editing with Replay minimizes this risk. Because the code is grounded in a video recording of the actual working system, the AI isn't guessing how the UI should behave. It is documenting and replicating reality. This is the "Surgical Precision" that saves companies millions in QA and refactoring costs.

If you are looking to tackle your technical debt or accelerate your design-to-code pipeline, you need to move beyond screenshots. You need the full context of video.

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