Vectorizing User Intent: How Replay Feeds High-Fidelity Data to Enterprise AI Agents

The $3.6 trillion global technical debt crisis isn't a coding problem; it’s a context problem. Most enterprise modernization efforts fail because the tribal knowledge required to understand a 20-year-old legacy system has vanished, leaving behind undocumented workflows and "black box" logic. When 67% of legacy systems lack any form of usable documentation, your AI agents aren't just hallucinating—they are starving for high-fidelity data.

Traditional RAG (Retrieval-Augmented Generation) attempts to modernize these systems by scraping static screenshots or parsing messy source code. This results in "low-resolution" intelligence. To build truly autonomous enterprise agents, we must move beyond static analysis. We need to focus on vectorizing user intent replay—the process of converting real human interaction with legacy UIs into structured, semantic data that AI can actually reason with.

Replay bridges this gap by using Visual Reverse Engineering to transform video recordings of legacy workflows into documented React components and structured architecture flows. By capturing the "how" and "why" of a user's journey, Replay provides the high-fidelity substrate required for the next generation of AI-driven modernization.

TL;DR:

• •The Problem: 70% of legacy rewrites fail because AI agents lack context and documentation for existing systems.
• •The Solution: Vectorizing user intent replay involves capturing video of legacy workflows and converting them into semantic embeddings for AI consumption.
• •The Impact: Replay reduces modernization timelines from 18-24 months to just weeks, cutting manual effort from 40 hours per screen to 4 hours.
• •The Result: High-fidelity React components, documented design systems, and AI-ready blueprints for regulated industries.

---

The High-Fidelity Data Gap in Legacy Modernization#

According to Replay's analysis, the average enterprise spends 18 months on a single major system rewrite, only to find that the new system misses 30% of the nuanced business logic present in the original. This happens because developers are forced to "guess" the intent behind legacy UI patterns.

When we talk about vectorizing user intent replay, we are describing the transition from "pixels" to "purpose." A standard AI model sees a button on a legacy screen as a coordinate on a grid. An AI agent fed with Replay data sees that same button as a trigger for a multi-stage validation logic in a high-compliance insurance claim workflow.

Video-to-code is the process of using computer vision and semantic analysis to extract functional UI components, state logic, and design tokens directly from a video recording of a legacy application.

Why Static Analysis Fails AI Agents#

Most AI agents used in modernization rely on static code analysis. However, in legacy environments (Mainframe, Delphi, Silverlight), the source code is often unavailable, obfuscated, or disconnected from the actual UI the user interacts with.

Industry experts recommend a "behavior-first" approach. By observing the system in motion, Replay captures:

1. •Temporal Logic: What happens after Step A but before Step B?
2. •Implicit Constraints: Which fields are required only under specific conditions?
3. •Visual Hierarchy: What elements are grouped together to form a logical "Component"?

---

How Vectorizing User Intent Replay Works#

The Replay platform functions as a sophisticated ingestion engine for enterprise AI. Instead of manually writing specifications, architects record a "Flow."

1. Visual Capture and Semantic Mapping#

As a user navigates a legacy system, Replay’s Visual Reverse Engineering engine identifies UI patterns. It doesn't just see a text box; it identifies an "Address Input Component" with specific validation rules. This is the first step in vectorizing user intent replay.

2. From Pixels to Blueprints#

Replay converts these visual patterns into "Blueprints"—intermediate representations of the UI that are framework-agnostic. These Blueprints are then processed by the AI Automation Suite to generate production-ready TypeScript and React code.

3. Creating the Vector Embeddings#

To feed an AI agent, Replay generates a semantic map of the workflow. This allows an LLM to "understand" the application's architecture at a granular level.

typescript
// Example: A vectorized representation of a legacy "Claim Submission" intent
interface UserIntentVector {
  intentId: string;
  sourceWorkflow: "Legacy_Claims_Portal_v2";
  actionSequence: {
    element: "Submit_Button";
    preConditions: ["Form_Validation_Success", "Auth_Token_Present"];
    postConditions: ["Navigate_To_Success_Page", "Trigger_Email_Notification"];
  };
  designTokens: {
    primaryColor: "#0056b3"; // Extracted from video frames
    spacingUnit: 8;
    fontFamily: "Inter, sans-serif";
  };
  componentMapping: {
    type: "Organism";
    name: "ClaimSummaryCard";
    complexityScore: 0.85;
  };
}

By providing this level of detail, Replay ensures that the generated code isn't just a visual copy, but a functional evolution. For more on how this integrates into your broader strategy, see our guide on Legacy Modernization Strategies.

---

Comparison: Manual vs. Replay-Driven Modernization#

The difference in efficiency is staggering. While manual modernization requires an army of business analysts and front-end developers, vectorizing user intent replay automates the most tedious 70% of the work.

---

Implementation: Feeding Replay Data to AI Agents#

To truly leverage the power of vectorizing user intent replay, enterprises integrate Replay's output into their CI/CD pipelines and AI development environments. The "Blueprints" generated by Replay serve as the ground truth for the new system.

Generating Modern Components#

When Replay processes a legacy recording, it outputs structured React components. Below is a simplified example of how Replay transforms a legacy "Data Grid" into a modern, accessible React component.

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

/**
 * Component: LegacyDataGrid
 * Generated via Replay Visual Reverse Engineering
 * Source: Insurance_Claims_Mainframe_Screen_04
 * Intent: Display and filter active claims
 */

interface ClaimData {
  id: string;
  policyNumber: string;
  status: 'pending' | 'approved' | 'rejected';
  amount: number;
}

export const ModernizedClaimsTable: React.FC<{ data: ClaimData[] }> = ({ data }) => {
  // Replay extracted the logic that 'status' color-coding was critical
  const getStatusColor = (status: string) => {
    switch (status) {
      case 'approved': return 'text-green-600';
      case 'rejected': return 'text-red-600';
      default: return 'text-amber-600';
    }
  };

  return (
    <div className="p-6 bg-white rounded-lg shadow-sm">
      <h2 className="text-xl font-bold mb-4">Active Claims Overview</h2>
      <table className="min-w-full divide-y divide-gray-200">
        <thead>
          <tr>
            <th className="px-4 py-2 text-left">Policy #</th>
            <th className="px-4 py-2 text-left">Status</th>
            <th className="px-4 py-2 text-right">Amount</th>
          </tr>
        </thead>
        <tbody>
          {data.map((claim) => (
            <tr key={claim.id} className="hover:bg-gray-50 transition-colors">
              <td className="px-4 py-2 font-mono">{claim.policyNumber}</td>
              <td className={`px-4 py-2 font-medium ${getStatusColor(claim.status)}`}>
                {claim.status.toUpperCase()}
              </td>
              <td className="px-4 py-2 text-right">${claim.amount.toLocaleString()}</td>
            </tr>
          ))}
        </tbody>
      </table>
    </div>
  );
};

This code isn't just "guessed." It is the result of Replay analyzing the visual states and interactions captured during the recording phase. This is how we achieve a 70% average time savings in modernization projects.

---

Architecting for Regulated Environments#

For industries like Financial Services, Healthcare, and Government, "cloud-only" AI is often a non-starter. Replay is built for these high-stakes environments. The process of vectorizing user intent replay can be performed entirely on-premise or within a VPC.

Security and Compliance#

• •SOC2 & HIPAA Ready: Data handling protocols that meet the strictest regulatory requirements.
• •PII Redaction: Replay’s AI Automation Suite can automatically mask sensitive data in video recordings before they are processed into vectors.
• •On-Premise Availability: Keep your legacy IP within your own firewall while still benefiting from AI-driven modernization.

According to Replay's internal benchmarks, organizations in the healthcare sector using Replay have seen a 90% reduction in compliance-related documentation errors during system migrations. By capturing the intent directly from the user's screen, the "Audit Trail" of how a component was derived is built-in.

---

Beyond Code: Building the Living Design System#

One of the most powerful outcomes of vectorizing user intent replay is the automatic generation of a Design System. Most legacy systems are a patchwork of different UI libraries and custom CSS. Replay’s "Library" feature extracts these patterns and normalizes them.

Visual Reverse Engineering is more than just code generation; it is the extraction of a brand's DNA from its functional history.

When you record a flow, Replay identifies:

• •Global Tokens: Typography, color palettes, and spacing.
• •Atomized Components: Buttons, inputs, and toggles.
• •Complex Organisms: Navigation bars, data tables, and modal workflows.

This allows organizations to move from a fragmented legacy UI to a unified, modern Design System in a fraction of the time.

---

The Future of Enterprise AI Agents with Replay#

The ultimate goal of vectorizing user intent replay is to enable autonomous agents that can maintain and evolve enterprise software. When your AI agent understands the intent of the legacy system, it can:

1. •Self-Heal: Identify when a modernized component deviates from the original business logic.
2. •Auto-Document: Generate living documentation that updates as the UI evolves.
3. •Predictive Refactoring: Suggest architectural improvements based on observed user friction in the legacy recordings.

By using Replay, you are not just performing a one-time migration. You are creating a high-fidelity data asset that serves as the foundation for your enterprise's AI future.

---

Frequently Asked Questions#

What does vectorizing user intent replay actually mean?#

It is the process of recording human interaction with a legacy application and using AI to convert those visual and temporal actions into machine-readable mathematical representations (vectors). These vectors allow AI agents to understand the semantic meaning, business logic, and architectural purpose of UI elements, rather than just seeing them as static images.

How does Replay handle highly complex legacy systems with no documentation?#

Replay's Visual Reverse Engineering engine does not require existing documentation or source code. It relies on the "ground truth" of the user interface. By recording a subject matter expert performing a standard workflow, Replay extracts the component hierarchy, state transitions, and design tokens directly from the video, effectively creating the documentation that was missing.

Can Replay generate code for frameworks other than React?#

While Replay is optimized for generating high-quality React and TypeScript code (the standard for modern enterprise web apps), the underlying "Blueprints" are framework-agnostic. These Blueprints can be used to inform the development of components in Vue, Angular, or even mobile frameworks like React Native, depending on the enterprise's architectural requirements.

Is my sensitive data safe during the recording process?#

Yes. Replay is built for regulated industries including Healthcare and Finance. We offer PII (Personally Identifiable Information) masking, SOC2 compliance, and HIPAA-ready environments. For organizations with the highest security requirements, Replay can be deployed on-premise, ensuring that no data ever leaves your secure network.

How does Replay achieve a 70% time savings?#

The majority of modernization time is spent on "Discovery" (understanding what the old system does) and "Scaffolding" (writing the initial code for hundreds of screens). Replay automates both. By converting a 10-minute video into a documented React component and architecture flow, it eliminates months of manual analysis and boilerplate coding.

---

Ready to modernize without rewriting? Book a pilot with Replay