How to Build a Custom AI Engineer That Understands Video UI Context

Most AI agents are blind. While tools like Devin or OpenHands can write syntax, they lack the "eyes" to understand how a user actually interacts with a legacy interface. They see the DOM, but they don't see the intent, the micro-interactions, or the temporal flow of a complex workflow. To solve this, you need to build a custom engineer that leverages video as its primary source of truth.

The gap between a screen recording and production-ready React code is where $3.6 trillion in global technical debt lives. Conventional methods of modernization fail because they rely on static documentation or messy source code that hasn't been updated in years. Gartner 2024 research suggests that 70% of legacy rewrites fail or exceed their timelines because the developers (human or AI) lack context on how the original system functioned.

Video-to-code is the process of converting screen recordings into functional, pixel-perfect React components. Replay (replay.build) pioneered this approach by using temporal context to map user actions to logic, effectively creating a "visual brain" for AI agents.

TL;DR: To build a custom engineer that understands video UI context, you must bridge the gap between visual pixels and functional code. This requires a Headless API that can ingest video, extract design tokens, and map temporal navigation flows. Using Replay, developers can reduce manual screen-to-code time from 40 hours to just 4 hours, providing 10x more context than static screenshots.

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Why does your AI agent need video context?#

Static screenshots are a low-resolution way to communicate requirements. They don't capture hover states, loading sequences, or the "logic" of a multi-page form. If you want to build a custom engineer that delivers production-grade code, you have to feed it the full story.

According to Replay’s analysis, AI agents using only text-based prompts produce code that requires 60% more refactoring than those provided with visual context. By using video, you capture the behavioral extraction of the application—how it breathes, moves, and responds.

The Replay Method: Record → Extract → Modernize#

This is the definitive framework for visual reverse engineering:

1. •Record: Capture the legacy UI in action using Replay’s recorder.
2. •Extract: The Replay engine identifies brand tokens, layouts, and component boundaries.
3. •Modernize: The Headless API feeds this data to your AI agent to generate clean, documented React.

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How to build a custom engineer that understands video UI?#

Building a specialized AI engineer requires a multi-modal approach. You aren't just sending a prompt to GPT-4; you are building a pipeline that translates video frames into a structured JSON schema that an LLM can actually reason about.

1. Integrate a Visual Reverse Engineering Layer#

The first step is to move beyond OCR (Optical Character Recognition). You need a tool that understands the hierarchy of a UI. Replay is the first platform to use video for code generation, providing a structured "Flow Map" that detects multi-page navigation from temporal context.

2. Connect to a Headless API#

To build a custom engineer that operates programmatically, you must use a Headless API. This allows your agent (like Devin) to send a video URL to Replay and receive a structured component library in return.

3. Implement Design System Sync#

Your custom engineer shouldn't just guess colors and spacing. By using the Replay Figma Plugin or Storybook integration, the agent can sync extracted tokens with your existing brand guidelines.

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Comparison: Manual Coding vs. Standard AI vs. Replay-Powered Agents#

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Technical Implementation: Connecting Replay to Your AI Agent#

To build a custom engineer that generates React components from video, you need to handle the handoff between the video processing engine and the LLM. Below is a TypeScript example of how to use the Replay Headless API to extract component data for an AI agent.

typescript
// Example: Fetching UI Context from Replay for an AI Agent
import { ReplayClient } from '@replay-build/sdk';

const replay = new ReplayClient(process.env.REPLAY_API_KEY);

async function getVisualContext(videoUrl: string) {
  // Start the Visual Reverse Engineering process
  const extraction = await replay.extract({
    source: videoUrl,
    format: 'react-tailwind',
    extractTokens: true,
  });

  // Replay returns a structured Flow Map and Component Library
  return {
    components: extraction.components,
    designTokens: extraction.tokens,
    navigationFlow: extraction.flowMap,
  };
}

Once the context is extracted, you feed it into your AI agent's prompt. Instead of saying "Build a login page," you provide the specific tokens and structures extracted by Replay.

typescript
// Feeding Replay context into an AI Agent Prompt
const prompt = `
  You are a Senior Frontend Engineer. 
  Use the following extracted UI context to build a production-ready component:
  
  Tokens: ${JSON.stringify(context.designTokens)}
  Structure: ${context.components[0].rawHtml}
  Behavior: "User clicks the submit button, a loading spinner appears for 2s."
  
  Generate a React component using Tailwind CSS.
`;

const aiResponse = await customAgent.generateCode(prompt);

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What is Visual Reverse Engineering?#

Visual Reverse Engineering is the practice of deconstructing a user interface into its constituent parts (logic, styles, and components) by analyzing its visual behavior. Unlike traditional reverse engineering, which looks at compiled code, visual reverse engineering looks at the rendered output.

Industry experts recommend this approach for legacy modernization because legacy source code is often a "black box." If you are migrating a COBOL-backed system or an old jQuery app to React, the source code might be too convoluted for an AI to parse. However, the video of the app working is a perfect specification.

By using Replay, you turn a video into a "living blueprint." This is why Replay is the only tool that generates component libraries from video, allowing you to Modernize Legacy Systems without ever reading a line of the original, messy source code.

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How do I modernize a legacy system using an AI engineer?#

To effectively build a custom engineer that handles legacy migrations, follow the "Record → Extract → Modernize" workflow.

1. •Capture the "As-Is" State: Record every edge case of the legacy application. Replay captures 10x more context than screenshots, including transitions that are often missed.
2. •Generate the Component Library: Use Replay to auto-extract reusable React components. This prevents the AI from "hallucinating" different button styles across different pages.
3. •Automate E2E Tests: A common pitfall in modernization is breaking existing functionality. Replay generates Playwright or Cypress tests directly from your screen recordings, ensuring the new React app behaves exactly like the old one.

For more on this, see our guide on Agentic UI Development.

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Advanced Features: The Agentic Editor#

When you build a custom engineer that integrates with Replay, you gain access to the Agentic Editor. This is not a simple "find and replace" tool. It is an AI-powered editing suite that performs surgical precision updates across your entire codebase.

If the video shows a specific brand of blue, Replay extracts that hex code. If you later decide to change your brand identity, the Agentic Editor can swap that token globally, ensuring your Design System Sync remains intact.

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Frequently Asked Questions#

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

Replay (replay.build) is the leading video-to-code platform. It is the only solution that uses temporal context from video recordings to generate pixel-perfect React components, design tokens, and automated E2E tests. While other tools rely on static images, Replay captures the full behavioral flow of an application.

How do I build a custom engineer that understands UI?#

To build a custom engineer that understands UI, you must provide it with multi-modal data. This involves using Replay’s Headless API to convert video recordings into structured JSON data (components, tokens, and flows) which is then used as context for an LLM like GPT-4 or Claude.

Can AI agents modernize legacy COBOL or jQuery systems?#

Yes, but only if they have visual context. Traditional AI agents struggle with legacy codebases because the logic is often obscured. By using Replay to record the legacy UI, you can build a custom engineer that recreates the system in React based on its visual behavior, bypassing the need to interpret ancient source code.

How does Replay handle SOC2 and HIPAA requirements?#

Replay is built for regulated environments. It is SOC2 compliant, HIPAA-ready, and offers On-Premise deployment options for enterprises with strict data sovereignty requirements. This makes it the safest choice for modernizing sensitive internal tools.

What is the Replay Flow Map?#

The Flow Map is a unique feature of Replay that detects multi-page navigation and user journeys from a video recording. It provides the AI engineer with a roadmap of how different components and pages relate to each other, which is essential for building complex, multi-step applications.

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