lesson-01

Learn how a team used multi-agent AI coding to modernize a legacy e-commerce platform with 500K lines of code.

Project Overview

Company: MegaShop (fictional) Challenge: Migrate from a monolithic PHP/jQuery application to a modern Next.js/Node.js architecture Timeline: 6 months with AI assistance (vs. estimated 18 months traditional) Team: 4 developers + AI agents

The Legacy System

Legacy Stack:
├── PHP 5.6 monolith
├── MySQL with 200+ tables
├── jQuery frontend (no components)
├── No test coverage
├── Tightly coupled business logic
└── 8 years of technical debt

Phase 1: Assessment with AI

The team used Claude Code to analyze the entire codebase:

# Initial codebase analysis
claude "Analyze this PHP codebase. Identify:
1. Core business domains
2. Database relationships
3. Integration points
4. Technical debt hotspots
5. Migration priorities"

AI-Generated Architecture Map

Identified Domains:
├── Product Catalog (45K LOC)
│   ├── High complexity
│   ├── Many integrations
│   └── Priority: Medium
├── Shopping Cart (15K LOC)
│   ├── Medium complexity
│   ├── Session-based state
│   └── Priority: High (customer-facing)
├── Checkout/Payments (30K LOC)
│   ├── Critical path
│   ├── PCI compliance required
│   └── Priority: Low (risk)
├── User Management (20K LOC)
│   ├── Low complexity
│   ├── Auth integration
│   └── Priority: High (foundation)
└── Order Management (25K LOC)
    ├── Complex workflows
    ├── ERP integration
    └── Priority: Medium

Phase 2: Strangler Fig Implementation

Setting Up the Migration Pattern

// API Gateway routing (new system)
// gateway/src/routes.ts

import { Router } from 'express';
import { legacyProxy } from './legacy-proxy';
import { newServices } from './services';

const router = Router();

// Migrated endpoints -> new services
router.use('/api/v2/users', newServices.users);
router.use('/api/v2/cart', newServices.cart);

// Not yet migrated -> proxy to legacy
router.use('/api', legacyProxy);

// Feature flag controlled migration
router.use('/api/v2/products', (req, res, next) => {
  if (featureFlags.isEnabled('new-product-service', req.user)) {
    return newServices.products(req, res, next);
  }
  return legacyProxy(req, res, next);
});

AI-Assisted Domain Extraction

# Extract user domain with Claude Code
claude "Extract the user management domain from this PHP codebase.
Create a new Node.js/TypeScript service that:
1. Implements the same API contracts
2. Uses the existing MySQL database
3. Adds proper TypeScript types
4. Includes comprehensive tests
5. Maintains backward compatibility"

Phase 3: Multi-Agent Workflow

The team orchestrated multiple AI agents for parallel work:

# Agent orchestration config
agents:
  architect:
    tool: claude-code
    role: "Design APIs and review PRs"

  backend-dev:
    tool: cursor
    role: "Implement Node.js services"

  frontend-dev:
    tool: cursor
    role: "Build React components"

  test-engineer:
    tool: claude-code
    role: "Generate test suites"

  migration-specialist:
    tool: devin
    role: "Data migration scripts"

Daily Workflow

Morning Standup (Human + AI):
1. Review overnight Devin migrations
2. Assign new features to Cursor agents
3. Claude Code reviews previous day's PRs

Development Cycle:
├── Human: Define feature requirements
├── Claude: Generate API specification
├── Cursor: Implement frontend + backend
├── Claude: Review and suggest improvements
├── Devin: Run integration tests overnight
└── Human: Final review and merge

Phase 4: Results and Metrics

Migration Progress

Month 1: Foundation
├── User service: 100% migrated
├── Auth system: 100% migrated
└── New CI/CD pipeline: Complete

Month 2-3: Core Commerce
├── Product catalog: 100% migrated
├── Shopping cart: 100% migrated
└── Search service: 100% migrated

Month 4-5: Complex Domains
├── Checkout flow: 100% migrated
├── Payment processing: 100% migrated
└── Order management: 80% migrated

Month 6: Completion
├── Remaining migrations: Complete
├── Legacy system: Decommissioned
└── Performance optimization: Complete

Key Metrics

Metric	Before	After	Improvement
Page Load Time	4.2s	0.8s	81% faster
API Response	450ms	85ms	81% faster
Test Coverage	0%	87%	New baseline
Deploy Frequency	Weekly	Daily	7x increase
Incident Rate	12/month	2/month	83% reduction

Lessons Learned

What Worked Well

1. AI-Driven Analysis: Claude Code identified patterns humans missed
2. Parallel Development: Multi-agent approach multiplied productivity
3. Strangler Fig: Zero-downtime migration with gradual rollout
4. Test Generation: AI created comprehensive test suites automatically

Challenges Encountered

1. Context Limits: Large files required chunking strategies
2. Legacy Quirks: Undocumented business rules needed human knowledge
3. Integration Testing: Complex scenarios required human oversight
4. Team Adoption: Learning curve for effective AI collaboration

Best Practices Established

## Team Guidelines

1. Always provide business context to AI agents
2. Review AI-generated code for security implications
3. Maintain human ownership of critical paths (payments)
4. Document AI decisions in ADRs
5. Use feature flags for all migrations

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