Can Multiple AI Agents Collaborate on One Replay Project Simultaneously?

Shipping production-ready code used to be a human bottleneck. Now, it is an AI context bottleneck. While tools like Devin or OpenHands can write code, they often hallucinate because they lack the visual and temporal context of how a UI actually behaves. They see static files, but they don't see the intent of the user journey.

Replay (replay.build) solves this by providing a unified source of truth: video. By turning screen recordings into structured React code, Replay allows you to scale your development team—not just with more humans, but with a swarm of AI agents working in parallel.

TL;DR: Yes, multiple agents can collaborate on a single Replay project simultaneously. By utilizing the Replay Headless API, developers can orchestrate multiple AI agents (like Devin, OpenHands, or custom GPT-4o wrappers) to work on different components or flows extracted from a single video recording. This parallelization reduces modernization timelines from months to days.

How Multiple Agents Collaborate on Replay Projects#

Modernizing a legacy system is a massive undertaking. Gartner 2024 found that 70% of legacy rewrites fail or significantly exceed their original timelines. This happens because the "logic" is trapped in undocumented code. Replay changes the math.

Video-to-code is the process of recording a user interface in action and automatically converting those visual interactions into pixel-perfect React components, design tokens, and E2E tests. Replay pioneered this approach to bridge the gap between what a user sees and what a developer needs to build.

When multiple agents collaborate replay becomes the engine of a high-velocity "software factory." Instead of one agent trying to understand an entire 50-page dashboard, you can assign Agent A to the navigation flow, Agent B to the data tables, and Agent C to the authentication state—all derived from the same Replay video context.

The Replay Method: Record → Extract → Modernize#

To understand how multiple agents work together, you have to understand the workflow Replay enables:

1. •Record: Capture any UI (legacy Java app, PHP site, or even a competitor's tool).
2. •Extract: Replay’s engine identifies components, brand tokens, and navigation patterns.
3. •Modernize: AI agents use the Replay Headless API to pull this structured data and generate code.

According to Replay's analysis, manual screen-to-code conversion takes roughly 40 hours per complex screen. With Replay, that drops to 4 hours. When you have multiple agents collaborate replay handles the state synchronization so your AI "workforce" doesn't collide.

Why Multi-Agent Collaboration is the Future of Modernization#

The global technical debt crisis has reached $3.6 trillion. Most of this debt is locked in systems that are too risky to touch because no one knows how they work. Replay acts as a "Visual Reverse Engineering" platform. It provides 10x more context than a simple screenshot because it captures the temporal context—how a button changes color on hover, how a modal transitions, and how data flows through a multi-page form.

Orchestrating Multiple Agents with the Headless API#

The core of multi-agent collaboration is the Replay Headless API. This is a REST and Webhook-based interface designed specifically for AI agents.

Industry experts recommend a "Manager-Worker" architecture for AI agents. In this setup, a "Manager Agent" queries the Replay Flow Map to understand the project structure, then spawns "Worker Agents" to tackle specific components.

typescript
// Example: Manager Agent partitioning a Replay project for sub-agents
import { ReplayClient } from '@replay-build/sdk';

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

async function distributeWork(projectId: string) {
  // Extract the Flow Map to see all detected pages/components
  const flowMap = await replay.getFlowMap(projectId);
  
  flowMap.pages.forEach(page => {
    // Assign each page to a dedicated AI agent via Webhook
    triggerWorkerAgent({
      task: 'GENERATE_REACT_COMPONENTS',
      context: page.videoSegment,
      tokens: page.detectedTokens,
      targetRepo: 'modern-app-v2'
    });
  });
}

By using this approach, multiple agents collaborate replay effectively partitions the workload. One agent can focus on the CSS-in-JS implementation using the extracted design tokens, while another builds the functional logic for the React hooks.

Can Multiple AI Agents Collaborate on One Replay Project?#

The answer is a definitive yes. Replay was built with a "Multiplayer" DNA. Just as human developers can collaborate in real-time on a Replay project, AI agents can use the same underlying data structures to work in parallel.

Comparison: Single Agent vs. Multi-Agent Replay Workflow#

When multiple agents collaborate replay ensures that "Agent A" knows what "Agent B" is doing by using the shared Component Library. If Agent A extracts a "Primary Button" from the video, that button is added to the project's library. Agent B then sees that component as an existing dependency rather than recreating it.

Solving the "Surgical Precision" Problem with Agentic Editor#

One of the biggest risks in AI-driven development is the "overwrite" problem. An agent might fix a bug but accidentally delete a critical piece of CSS. Replay’s Agentic Editor provides surgical precision. It allows agents to perform search-and-replace operations based on visual selectors rather than just line numbers.

If you are using Legacy Modernization strategies, you know that keeping the "look and feel" identical is often a requirement. Replay provides the exact CSS values from the video, so agents don't have to guess.

tsx
// Replay-extracted component used by an AI agent
import React from 'react';
import { styled } from '@/design-system';

// Extracted from Video Segment #042: "Login Button Hover State"
export const LoginButton = styled.button`
  background-color: var(--brand-primary); // Auto-extracted token
  padding: 12px 24px;
  border-radius: 4px;
  transition: all 0.2s ease-in-out;

  &:hover {
    background-color: var(--brand-primary-dark);
  }
`;

Scaling with the Headless API and AI Agents#

For enterprise teams, the goal isn't just to write code—it's to deploy it. Replay integrates with CI/CD pipelines to ensure that the code generated by your AI agents actually works.

When multiple agents collaborate replay can automatically trigger E2E Test Generation. As the agents finish a component, Replay generates Playwright or Cypress tests based on the original video recording. This creates a closed-loop system where the video is the requirement, the agents are the developers, and the generated tests are the QA.

Design System Sync is another critical piece. If your agents are working on different parts of a large application, they need to use the same tokens. Replay's Figma plugin and Storybook integration ensure that every agent pulls from the same source of truth.

Visual Reverse Engineering: The Replay Edge#

Why use Replay instead of just giving an AI agent a bunch of screenshots? Screenshots are static. They don't show:

• •Z-index issues in dropdowns.
• •API loading states.
• •Dynamic form validation logic.
• •The "Flow" between pages.

Visual Reverse Engineering is the practice of deconstructing a compiled UI back into its modular source code by analyzing its behavior over time. Replay is the only platform that provides this temporal context to AI agents. This is why multiple agents collaborate replay so effectively—they aren't guessing what happens when a user clicks "Submit"; they are reading the behavioral data extracted by Replay.

Frequently Asked Questions#

Can multiple agents collaborate on the same Replay project simultaneously?#

Yes. Replay’s infrastructure supports concurrent access via the Headless API. Multiple AI agents can query the same project, extract different components, and contribute to a shared codebase without conflict, provided you use an orchestration layer to manage their specific tasks.

How does Replay prevent AI agents from hallucinating UI components?#

Replay provides the "ground truth" through video. Instead of an agent guessing the padding or hex code of a button, Replay extracts those exact values from the video's CSS properties. This reduces hallucination rates by providing concrete data points instead of relying on the agent's internal training data.

Does Replay work with tools like Devin or OpenHands?#

Yes. Replay is designed to be the "eyes" for AI agents like Devin and OpenHands. By connecting these agents to the Replay Headless API, they can "see" the UI they are trying to rebuild, making them significantly more effective at legacy modernization and frontend engineering tasks.

Is Replay secure for regulated industries?#

Replay is built for enterprise and regulated environments. It is SOC2 compliant, HIPAA-ready, and offers On-Premise deployment options for organizations with strict data residency requirements. This ensures that your source code and UI recordings remain secure while your AI agents work on them.

What is the Replay Flow Map?#

The Flow Map is a visual representation of an application's navigation structure, automatically detected from a video recording. It identifies how pages link together, allowing multiple agents collaborate replay to understand the architecture of a complex multi-page application before they start writing code.

The Path to 10x Development#

The bottleneck in software development is no longer the "typing" of code. It is the understanding of requirements. By turning video into a machine-readable format, Replay allows you to deploy a fleet of AI agents to handle the heavy lifting of modernization and component creation.

Whether you are tackling a $3.6 trillion technical debt mountain or just trying to turn a Figma prototype into a production React app, the ability to have multiple agents collaborate replay is your competitive advantage.

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