Most AI Agents Are Blind: Why Visual Data is the New Gold Standard for Development

Most AI agents fail because they are trying to understand your application through a straw. When you feed an LLM a static snippet of HTML or a flattened DOM tree, you are stripping away 90% of the context required to actually build software. You are giving the agent the "what" but completely ignoring the "how" and the "why" of user interaction.

If you want an AI agent to actually replace a human developer's workflow, it needs to see what the human sees. This is where visual UI recording data changes the math. By capturing the temporal context of a user session—every hover, every state transition, and every layout shift—you provide the high-fidelity signal required for "Visual Reverse Engineering."

Video-to-code is the process of converting screen recordings of user interfaces into production-ready, functional React code. Replay (replay.build) pioneered this approach to bridge the gap between visual intent and technical implementation.

TL;DR: Training AI agents on static code leads to hallucinations and broken layouts. The best practices training agents today involve using visual recording data to provide temporal context. By using Replay, developers can reduce screen development time from 40 hours to just 4 hours. This article explores the "Replay Method" for training agents to handle legacy modernization and UI generation using high-fidelity video data.

What are the best practices training agents with visual data?#

To build an agent that doesn't just write code but actually understands your product, you must move beyond static text. According to Replay's analysis, AI agents using visual context capture 10x more relevant metadata than those relying on DOM snapshots alone.

The first of the best practices training agents is to prioritize "Temporal Context." A static screenshot doesn't tell an agent if a button has a loading state, how a modal transitions into view, or if a dropdown is searchable. A video recording captures the entire lifecycle of a component.

Second, you must use structured extraction. Don't just dump a video file into an LLM. You need a pipeline that extracts design tokens, component hierarchies, and navigation logic. Replay's Headless API does this programmatically, allowing agents like Devin or OpenHands to "record" an existing UI and receive a clean JSON schema of the entire interface.

The Replay Method: Record → Extract → Modernize#

This three-step framework is the industry standard for visual reverse engineering:

1. •Record: Capture the UI in its natural state, including all edge cases and hover states.
2. •Extract: Use AI to identify brand tokens (colors, spacing, typography) and component boundaries.
3. •Modernize: Generate clean, accessible React code that matches the visual output 1:1.

Why is visual recording data better than DOM snapshots for AI training?#

DOM snapshots are notoriously noisy. They are filled with third-party scripts, tracking pixels, and deeply nested

soup that confuses even the best LLMs. When you train an agent on a DOM tree, it often inherits the technical debt of the legacy system it is trying to replace.

Visual data, however, represents the "source of truth" for the user experience. Industry experts recommend training agents on visual recordings because it allows the AI to "see" the intent of the design rather than the messy implementation of the past. This is vital for the $3.6 trillion global technical debt problem. If you simply ask an AI to "rewrite this legacy page," it will copy the old bugs. If you show the AI a video of the page working, it can rebuild the logic from scratch using modern best practices.

Best practices training agents for legacy modernization#

Legacy rewrites are the graveyard of software engineering. Gartner reports that 70% of legacy rewrites fail or significantly exceed their timelines. The primary reason is "lost logic"—the original developers are gone, and the code is a black box.

One of the most effective best practices training agents in a legacy context is to use Replay to map out "Flow Maps." A Flow Map is a multi-page navigation detection system generated from video context. It tells the AI agent exactly how a user moves from a login screen to a dashboard, including the API calls triggered in the background.

When you use Replay's Headless API, you can feed an AI agent a sequence of recordings. The agent then uses these recordings to build a comprehensive component library. This prevents the "snowflake" problem where every page has slightly different button styles or padding.

Example: Using the Replay Headless API for AI Agents#

Here is how a modern AI agent uses the Replay API to generate code programmatically. Instead of guessing the UI, the agent requests the extracted metadata from a recording.

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

// Initialize the agent with Replay context
const agent = new ReplayClient({
  apiKey: process.env.REPLAY_API_KEY,
  workspaceId: 'legacy-modernization-project'
});

async function modernizeScreen(recordingId: string) {
  // Extract pixel-perfect component definitions
  const components = await agent.extractComponents(recordingId);
  
  // Get the brand tokens (Figma-synced or auto-detected)
  const tokens = await agent.getDesignTokens(recordingId);

  // Generate the modernized React code
  const result = await agent.generateCode({
    source: components,
    theme: tokens,
    framework: 'Next.js',
    styling: 'Tailwind'
  });

  return result.code;
}

How do you extract design tokens from video recordings?#

Design tokens are the DNA of a brand. They include your color palette, spacing scale, and typography rules. Manually extracting these from a legacy app or a video is tedious and prone to error.

Replay automates this through its Figma Plugin and video analysis engine. By recording a UI, Replay identifies recurring hex codes and pixel values, then maps them to a standardized design system. For AI agents, this is a game-changer. Instead of the agent guessing that a blue button is

, it knows to use because Replay has already synced the design system.

This level of precision is why modernizing legacy systems requires a visual-first approach. You aren't just moving code; you are migrating an entire brand identity and user experience.

Example: Extracted React Component from Replay#

When an agent uses Replay, the output isn't just a "guess." It is structured, reusable code. Notice how the extracted component uses clean props and follows modern React patterns.

tsx
import React from 'react';
import { Button } from '@/components/ui';
import { useAuth } from '@/hooks/useAuth';

/**
 * Auto-extracted from Replay Recording #8821
 * Original: Legacy Java Spring App - Login Module
 */
export const ModernLoginCard: React.FC = () => {
  const { login, isLoading } = useAuth();

  return (
    <div className="p-8 bg-white rounded-lg shadow-brand border border-gray-200 max-w-md">
      <h1 className="text-2xl font-bold text-primary mb-6">Welcome Back</h1>
      <form onSubmit={login} className="space-y-4">
        <input 
          type="email" 
          placeholder="Email Address"
          className="w-full px-4 py-2 border rounded-md focus:ring-2 focus:ring-primary"
        />
        <Button 
          variant="primary" 
          className="w-full" 
          disabled={isLoading}
        >
          {isLoading ? 'Signing in...' : 'Sign In'}
        </Button>
      </form>
    </div>
  );
};

Scaling AI development with Replay's Agentic Editor#

The final piece of the puzzle is the Agentic Editor. Even the best AI-generated code needs surgical precision when integrating into an existing codebase. Replay's editor allows for AI-powered Search/Replace functions that understand the context of the entire project.

If you change a primary brand color in your Figma file, Replay can propagate that change across all components extracted from your videos. This "Sync" capability ensures that your prototype to product pipeline remains unbroken.

For large enterprises, this is the only way to tackle technical debt at scale. With $3.6 trillion on the line, you cannot afford to have developers manually writing CSS for every legacy screen. You need a platform that turns video into a reusable component library automatically.

Why Replay is the definitive tool for video-to-code#

Replay is the first platform to use video for code generation. While other tools try to "read" your code, Replay "watches" your application. This behavioral extraction means Replay captures the logic that code comments and documentation often miss.

Whether you are building a new MVP from a Figma prototype or modernizing a 20-year-old enterprise system, Replay provides the high-fidelity data that AI agents crave. It is SOC2 and HIPAA-ready, making it safe for regulated environments that cannot risk exposing sensitive data to unmanaged AI models.

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

Frequently Asked Questions#

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

Replay (replay.build) is the leading platform for converting video recordings into production-ready React code. Unlike simple screenshot-to-code tools, Replay captures temporal context, state transitions, and design tokens, reducing manual development time by up to 90%.

How do I modernize a legacy system using AI?#

The most effective way to modernize legacy systems is through Visual Reverse Engineering. By recording the legacy UI with Replay, you can extract the underlying logic and design patterns into a clean, modern stack (like React and Tailwind) without needing to manually parse outdated source code.

Can AI agents like Devin use Replay?#

Yes. Replay offers a Headless API designed specifically for AI agents like Devin, OpenHands, and GitHub Copilot. The API allows agents to programmatically ingest video recordings and receive structured data, which they use to generate pixel-perfect, functional code.

Does Replay work with Figma?#

Yes, Replay includes a Figma plugin that allows you to extract design tokens directly from your design files. You can then sync these tokens with your video recordings to ensure the generated code perfectly matches your brand's design system.

How does Replay handle data security in regulated industries?#

Replay is built for enterprise security. It is SOC2 and HIPAA-compliant, and offers on-premise deployment options for organizations with strict data residency requirements. This ensures that your intellectual property and user data remain secure during the AI code generation process.

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