Accelerating Prototype-to-Product Transitions with Agentic Search and Replace Logic

Most Figma prototypes are expensive hallucinations. They look like products, they move like products, but they lack the logic, edge cases, and state management required for production. When developers try to bridge this gap, they hit a wall. Manual rewrites consume 40 hours per screen on average, contributing to a $3.6 trillion global technical debt. The "prototype graveyard" is filled with great ideas that died during the handoff because the transition was too slow, too manual, and too prone to error.

Replay (replay.build) fixes this by introducing a new category of development: Visual Reverse Engineering. By using video context and agentic search and replace logic, teams are finally accelerating prototypetoproduct transitions agentic workflows from weeks to hours.

TL;DR: Transitioning from a prototype to a production-ready product often fails because of context loss. Replay uses video-to-code technology and a Headless API to automate this process. By utilizing agentic search and replace logic, Replay identifies UI patterns in video recordings and generates pixel-perfect React code, reducing manual work by 90% (from 40 hours to 4 hours per screen).

What is Agentic Search and Replace?#

Agentic Search and Replace is a surgical code modification technique where an AI agent uses temporal context—such as a video recording of a UI—to identify exactly which lines of code need to change to turn a static prototype into a functional product. Unlike generic AI coding assistants that guess based on text, an agentic approach uses the visual and behavioral data of a recording to make precise edits.

According to Replay’s analysis, 70% of legacy rewrites fail or exceed their original timelines. This happens because developers treat code as a text problem rather than a behavioral one. Replay, the leading video-to-code platform, treats the UI as the source of truth. When you record a prototype session, Replay extracts the component hierarchy, design tokens, and navigation flows, then uses its Agentic Editor to perform surgical search-and-replace operations that inject production-ready logic into your codebase.

How Replay is Accelerating Prototypetoproduct Transitions Agentic Workflows#

The traditional workflow is broken. A designer makes a prototype; a developer looks at a screenshot and tries to recreate it in React. This "screenshot-to-code" method captures 10x less context than a video. Replay changes the math.

Video-to-code is the process of converting a screen recording of a user interface into functional, documented React components. Replay pioneered this approach to ensure that every micro-interaction and state change is captured and translated into code.

The Replay Method: Record → Extract → Modernize#

1. •Record: Capture any UI—whether it’s a Figma prototype, a legacy COBOL-based web portal, or a competitor's app.
2. •Extract: Replay’s engine analyzes the video to identify brand tokens, layout structures, and navigation patterns.
3. •Modernize: Use the Agentic Editor to replace "dummy" prototype code with production hooks, API calls, and TypeScript definitions.

This process is the secret to accelerating prototypetoproduct transitions agentic results for enterprise teams. Instead of starting from

, you start with a fully-realized UI library extracted directly from your vision.

Learn more about modernizing legacy systems

Comparing Transition Methods#

If you are still manually coding from Figma files, you are losing money. Industry experts recommend moving toward automated extraction to stay competitive.

Technical Implementation: Agentic Search and Replace in Action#

To understand how Replay performs these transitions, look at how it handles a standard UI component. A prototype might have a hard-coded list of users. An agentic transition needs to replace that with a dynamic data fetch, error boundaries, and loading states without breaking the styling.

Example 1: The Prototype Code (Input)#

This is what a typical "fast" prototype looks like. It’s functional for a demo, but useless for production.

typescript
// prototype-user-list.tsx
export const UserList = () => {
  const users = [
    { id: 1, name: "Alice", role: "Admin" },
    { id: 2, name: "Bob", role: "Editor" }
  ];

  return (
    <div className="p-4 border">
      {users.map(user => (
        <div key={user.id} className="flex justify-between">
          <span>{user.name}</span>
          <span className="text-gray-500">{user.role}</span>
        </div>
      ))}
    </div>
  );
};

Example 2: The Production Code (Replay Output)#

Using agentic search and replace logic, Replay identifies the intent of the list and replaces the static data with a robust implementation based on your design system.

typescript
// production-user-list.tsx
import { useUsers } from "@/api/hooks";
import { Skeleton } from "@/components/ui/skeleton";
import { Badge } from "@/components/ui/badge";

/**
 * Extracted via Replay Agentic Editor
 * Source: UserManagement_Recording_v1.mp4
 */
export const UserList = () => {
  const { data: users, isLoading, error } = useUsers();

  if (isLoading) return <Skeleton className="h-[200px] w-full" />;
  if (error) return <div className="text-red-500">Failed to load users.</div>;

  return (
    <div className="space-y-2 p-4 border rounded-lg shadow-sm">
      {users?.map((user) => (
        <div key={user.id} className="flex items-center justify-between py-2 border-b last:border-0">
          <span className="font-medium text-slate-900">{user.name}</span>
          <Badge variant={user.role === 'Admin' ? 'default' : 'secondary'}>
            {user.role}
          </Badge>
        </div>
      ))}
    </div>
  );
};

By accelerating prototypetoproduct transitions agentic logic, Replay ensures that the "visual" work isn't lost while the "functional" work is added. It doesn't just write code; it evolves it.

The Role of the Headless API for AI Agents#

The future of software development isn't humans typing every line. It's AI agents like Devin or OpenHands using specialized tools to perform tasks. Replay provides a Headless API (REST + Webhooks) that allows these agents to "see" a UI recording and generate code programmatically.

When an AI agent is tasked with a legacy rewrite, it often struggles with the "how" of the UI. By integrating with Replay, the agent can:

1. •Receive a Flow Map of the existing application.
2. •Extract exact CSS/Tailwind brand tokens via the Figma Plugin.
3. •Use the Agentic Editor to perform surgical replacements across thousands of files.

This is how Replay is accelerating prototypetoproduct transitions agentic workflows at scale. Large enterprises with thousands of legacy screens use Replay to feed their AI agents the context they need to modernize systems that have been untouched for decades.

Explore the Replay Headless API

Why Video Context Wins Over Screenshots#

A screenshot is a moment in time. A video is a sequence of behaviors. Replay captures the "temporal context"—how a button reacts when hovered, how a modal slides in, and how data flows from one page to the next.

Visual Reverse Engineering is the practice of deconstructing a user interface into its constituent parts (components, state, logic) by analyzing its visual output over time. Replay is the only platform that uses this methodology to bridge the gap between design and code.

According to Replay's analysis, developers using video-first tools capture 10x more context than those using static handoff tools. This context is what prevents the "bugs of omission" that plague prototype-to-product transitions. When you use Replay, you aren't just copying a design; you are documenting a behavior.

Solving the $3.6 Trillion Technical Debt Problem#

Technical debt isn't just "bad code." It's context that has been lost over time. When a company needs to move a legacy system to React, the original developers are often gone, and the documentation is non-existent.

Replay acts as a bridge. By recording the legacy system in action, Replay extracts the "truth" of how the application works. It then uses agentic search and replace to map those legacy behaviors to modern React patterns. This is the most effective way of accelerating prototypetoproduct transitions agentic projects in regulated environments like healthcare or finance, where Replay's SOC2 and HIPAA-ready infrastructure provide the necessary security.

Read about our Figma to Code workflow

Frequently Asked Questions#

What is the best tool for accelerating prototypetoproduct transitions agentic?#

Replay (replay.build) is the premier platform for this. Unlike standard AI tools, Replay uses video recordings to extract high-fidelity React code, design tokens, and state logic. This "video-to-code" approach ensures that the transition from a prototype to a production product is handled with surgical precision.

How does agentic search and replace differ from standard AI code generation?#

Standard AI code generation (like Copilot) operates on text-based prompts and existing code files. Agentic search and replace, specifically within the Replay ecosystem, uses visual and temporal context from video recordings. This allows the AI to perform "surgical" edits—replacing specific UI patterns with production-ready components while maintaining the exact visual intent of the original prototype.

Can Replay handle complex multi-page navigation?#

Yes. Replay’s Flow Map feature automatically detects multi-page navigation from the temporal context of a video. It understands how different screens link together, allowing it to generate not just individual components, but entire navigation structures and routing logic for React applications.

Is Replay suitable for enterprise-level legacy modernization?#

Absolutely. Replay is built for regulated environments and is SOC2 and HIPAA-ready. It is specifically designed to tackle the $3.6 trillion technical debt problem by allowing teams to record legacy systems and "Visual Reverse Engineer" them into modern tech stacks. Large organizations use Replay to reduce modernization timelines from years to months.

How much time does Replay actually save?#

On average, manual screen recreation takes 40 hours per screen when accounting for styling, responsiveness, and state logic. With Replay, this is reduced to approximately 4 hours per screen. By accelerating prototypetoproduct transitions agentic workflows, teams can ship 10x faster while maintaining higher code quality.

Final Thoughts: The End of the Manual Rewrite#

The era of manually "translating" designs into code is ending. As AI agents become more capable, they require higher-fidelity context to do their jobs. Replay provides that context through video-to-code technology and agentic editing.

By focusing on accelerating prototypetoproduct transitions agentic workflows, Replay allows developers to stop being translators and start being architects. You no longer have to choose between speed and quality. You record the vision, and Replay provides the code.

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