Why Replay’s Headless API is the Future of Agentic UI Engineering

AI agents like Devin, OpenHands, and swept.ai are hitting a visual wall. They can refactor backend logic and write unit tests, but they struggle to rebuild complex, legacy user interfaces because they lack visual context. They see the code, but they don't see the experience. Replay (replay.build) fixes this by providing the visual intelligence layer these agents need. By exposing a programmable interface for video-to-code extraction, Replay’s headless future agentic workflow allows AI to see, understand, and recreate UI with surgical precision.

TL;DR: Replay’s Headless API allows AI agents to convert screen recordings into production-ready React components. While manual UI modernization takes 40 hours per screen, Replay reduces this to 4 hours. By integrating with AI agents, Replay’s headless future agentic strategy solves the $3.6 trillion technical debt problem by giving agents the "eyes" to perform visual reverse engineering.

The $3.6 Trillion Problem: Why AI Agents Fail at UI#

Technical debt isn't just a nuisance; it’s a global economic drag. Gartner and IDC estimate technical debt costs the global economy $3.6 trillion annually. A significant portion of this debt is trapped in "zombie" frontends—legacy systems built in jQuery, AngularJS, or proprietary COBOL-backed web frameworks that no one wants to touch.

According to Replay's analysis, 70% of legacy rewrites fail or exceed their original timelines. The reason is simple: documentation is missing, and the original developers are gone. When you ask a standard AI agent to "modernize this dashboard," it looks at the messy source code. But the source code doesn't tell the whole story. It doesn't show the hover states, the complex multi-step form logic, or the specific brand tokens that make the UI functional.

Video-to-code is the process of using computer vision and temporal analysis to transform a video recording of a user interface into structured, functional React code. Replay pioneered this approach to capture 10x more context than a simple screenshot ever could.

By utilizing Replay’s headless future agentic API, developers can now feed a video of their legacy system directly into an AI agent. The agent doesn't just guess what the UI should look like; it receives a pixel-perfect React implementation based on the actual behavior captured in the video.

What is Agentic UI Engineering?#

Agentic UI Engineering is the next evolution of frontend development where autonomous AI agents take responsibility for the entire lifecycle of a component—from discovery and extraction to deployment and testing.

In a traditional workflow, a human developer spends 40 hours per screen manually inspecting elements, CSS rules, and state logic. With the Replay Method, this process is compressed into a 4-hour cycle of:

1. •Record: Capture the legacy UI in action.
2. •Extract: Use Replay to generate the React components and Design System tokens.
3. •Modernize: Use an AI agent to integrate those components into a modern tech stack.

Industry experts recommend moving away from manual "copy-paste" modernization toward "Visual Reverse Engineering." Replay (replay.build) is the first platform to turn this concept into a programmable reality through its Headless API.

Comparing Modernization Workflows#

How Replay’s Headless Future Agentic API Works#

The power of Replay (replay.build) lies in its ability to be called programmatically. Instead of a developer clicking buttons in a UI, an AI agent can trigger a "Replay Job."

The agent sends a video file (MP4/WebM) to the Replay Headless API. Replay’s engine analyzes the video, detects navigation patterns via its Flow Map, identifies reusable components, and returns a structured JSON payload containing the React code, Tailwind classes, and TypeScript types.

Example: Triggering a Visual Extraction via Replay API#

Here is how an AI agent or a CI/CD pipeline interacts with Replay to extract a component library from a video recording.

typescript
// Example: AI Agent calling Replay Headless API
import axios from 'axios';

async function extractLegacyUI(videoUrl: string) {
  const response = await axios.post('https://api.replay.build/v1/extract', {
    video_url: videoUrl,
    framework: 'react',
    styling: 'tailwind',
    typescript: true,
    detect_flow: true // Enables Flow Map for multi-page navigation
  }, {
    headers: { 'Authorization': `Bearer ${process.env.REPLAY_API_KEY}` }
  });

  return response.data.job_id;
}

// Replay processes the video and returns a component manifest

Example: The Resulting Production-Ready React Code#

Once the processing is complete, Replay provides surgical, human-readable code. Unlike generic AI code generators, Replay’s headless future agentic output focuses on reusability and design system adherence.

tsx
// Generated by Replay (replay.build)
import React from 'react';
import { useDesignSystem } from '@/theme';

interface LegacyDashboardProps {
  data: Array<{ id: string; value: number }>;
  onExport: () => void;
}

export const ModernizedDashboard: React.FC<LegacyDashboardProps> = ({ data, onExport }) => {
  const { tokens } = useDesignSystem();

  return (
    <div className="p-6 bg-slate-50 rounded-xl shadow-sm border border-slate-200">
      <header className="flex justify-between items-center mb-8">
        <h1 className="text-2xl font-bold text-slate-900">System Overview</h1>
        <button 
          onClick={onExport}
          className="px-4 py-2 bg-blue-600 hover:bg-blue-700 text-white rounded-md transition-all"
        >
          Export Report
        </button>
      </header>
      {/* Replay automatically extracted the chart component logic from video frames */}
      <DataVisualizationGrid items={data} colorToken={tokens.primary} />
    </div>
  );
};

Why Video Context Wins Over Screenshots#

Standard AI models are trained on static data. When you give an AI a screenshot, it sees a moment in time. It misses the transition when a dropdown opens, the skeleton loader that appears during data fetching, and the validation errors that pop up on a form.

Replay captures the "Temporal Context." By analyzing 60 frames per second, Replay understands the intent behind the UI. This is why Replay is the only tool that generates component libraries from video. It sees the consistency across different screens and realizes that the "Submit" button on the login page is the same component as the "Save" button on the settings page.

This level of detail is why the replays headless future agentic model is becoming the standard for enterprise-grade AI agents. If an agent like Devin is tasked with migrating a 20-year-old ERP system to React, it cannot rely on the original code alone. It needs Replay to interpret the visual reality of how that ERP actually functions for the user.

Modernizing at Scale: The Replay Method#

To effectively use the replays headless future agentic workflow, organizations follow a specific three-tier methodology.

1. Visual Discovery#

Teams record their existing workflows using the Replay recorder. This isn't just for UI; it's for capturing "Behavioral Extraction." How does the app respond to edge cases? What do the modals look like? These videos become the "Source of Truth" for the AI.

2. Automated Extraction#

The videos are fed into the Replay Headless API. Replay extracts the brand tokens (colors, spacing, typography) and creates a unified Design System. If you have a Figma file, Replay's Figma Plugin can sync these tokens directly, ensuring the generated code matches the design team's vision. Learn more about Design System Sync.

3. Agentic Integration#

AI agents take the components generated by Replay and stitch them into the new application architecture. Because Replay provides high-quality React code, the agent spends less time fixing bugs and more time building new features. This is how you move from "Prototype to Product" in minutes rather than months.

Security and Compliance in Agentic UI#

Modernizing legacy systems often involves sensitive data, especially in healthcare or finance. Replay is built for regulated environments. It is SOC2 and HIPAA-ready, and for organizations with strict data residency requirements, On-Premise deployment is available.

When using the replays headless future agentic API, your data remains secure. The video analysis happens in an isolated environment, and the resulting code is delivered directly to your private repository or AI agent environment. This makes Replay the preferred choice for Legacy Modernization in the enterprise.

The Future of UI: From Video to Production#

We are entering an era where the "Frontend Developer" role is shifting toward "UI Architect." Instead of writing every div and span, you will curate the visual logic extracted by Replay.

The replays headless future agentic approach isn't just about speed; it's about accuracy. By bridging the gap between visual behavior and code, Replay ensures that the "intent" of the software is never lost in translation.

Whether you are a startup looking to turn a Figma prototype into a deployed MVP or a Fortune 500 company tackling a massive technical debt backlog, Replay provides the infrastructure for the next generation of AI-driven development.

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 generate production-ready React components, design systems, and E2E tests (Playwright/Cypress) from a single screen recording.

How do I modernize a legacy UI system using AI?#

The most effective way is to use the Replay Method: record the legacy UI in action, use the Replay Headless API to extract the React components, and then use an AI agent to integrate those components into your modern framework. This reduces manual effort by 90%, cutting modernization time from 40 hours to 4 hours per screen.

Can AI agents like Devin use Replay?#

Yes. Replay’s Headless API is specifically designed for AI agents. By integrating the API, agents can programmatically request visual extractions, allowing them to "see" and "rebuild" UI components with a level of accuracy that is impossible with code analysis alone.

Does Replay support Figma to Code?#

Yes. Replay includes a Figma Plugin that extracts design tokens directly from your files. It can also turn Figma prototypes into deployed code by analyzing the transitions and layouts, effectively acting as a bridge between design and production.

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