Migrating from a monolith to microservices is one of the riskiest endeavors in software engineering. It's expensive, time-consuming, and full of surprises. AI agents can help reduce that risk—not by doing the migration for you, but by handling the tedious analysis and transformation work.
Why Migrations Fail#
Most monolith-to-microservices migrations fail for predictable reasons:
- Poor boundary identification: Services are cut incorrectly
- Hidden dependencies: Connections discovered too late
- Data migration complexity: Shared databases are hard to split
- Testing gaps: Not enough tests to validate changes
- Big bang approach: Trying to do everything at once
AI can help with all of these.
Phase 1: Dependency Analysis#
Before cutting anything, understand what you have.
Automated Dependency Mapping#
AI analyzes your codebase to create a dependency graph:
1// AI-powered analysis
2const analysis = await aiAgent.analyzeMonolith({
3 entryPoint: './src',
4 output: './analysis'
5});
6
7// Output structure
8{
9 "modules": {
10 "users": {
11 "files": ["src/users/**/*.ts"],
12 "internalDependencies": ["utils", "database"],
13 "externalDependencies": ["bcrypt", "jsonwebtoken"],
14 "dependsOn": ["database"],
15 "usedBy": ["orders", "notifications", "billing"]
16 },
17 "orders": {
18 "files": ["src/orders/**/*.ts"],
19 "internalDependencies": ["users", "products", "database"],
20 "externalDependencies": ["stripe"],
21 "dependsOn": ["users", "products", "database"],
22 "usedBy": ["notifications", "analytics"]
23 }
24 // ... more modules
25 },
26 "circularDependencies": [
27 ["users", "billing", "users"]
28 ],
29 "suggestedBoundaries": [
30 {
31 "service": "user-service",
32 "modules": ["users", "auth"],
33 "reason": "Cohesive user identity domain"
34 }
35 ]
36}Visualizing the Architecture#
AI generates architecture diagrams:
1graph TD
2 A[API Gateway] --> B[Users Module]
3 A --> C[Orders Module]
4 A --> D[Products Module]
5
6 B --> E[(Users DB)]
7 C --> E
8 C --> F[(Orders DB)]
9 D --> E
10 D --> G[(Products DB)]
11
12 C --> B
13 C --> D
14
15 style B fill:#f9f,stroke:#333
16 style C fill:#bbf,stroke:#333
17 style D fill:#bfb,stroke:#333Phase 2: Boundary Identification#
AI suggests service boundaries based on:
- Code cohesion analysis
- Data access patterns
- Business domain alignment
- Change frequency correlation
1const boundaries = await aiAgent.suggestServiceBoundaries({
2 codebase: './src',
3 strategy: 'domain-driven',
4 constraints: {
5 maxServicesFirstPhase: 5,
6 minimumCohesion: 0.7,
7 avoidCircularDependencies: true
8 }
9});
10
11// AI output
12{
13 "recommendations": [
14 {
15 "serviceName": "identity-service",
16 "modules": ["users", "auth", "sessions"],
17 "rationale": "User identity is a bounded context with clear inputs/outputs",
18 "estimatedComplexity": "medium",
19 "dependencies": [],
20 "dependents": ["orders", "notifications", "billing"],
21 "dataStores": ["users table", "sessions table"],
22 "apis": [
23 "POST /auth/login",
24 "POST /auth/register",
25 "GET /users/:id"
26 ]
27 },
28 {
29 "serviceName": "order-service",
30 "modules": ["orders", "cart", "checkout"],
31 "rationale": "Order lifecycle is self-contained after user identity",
32 "estimatedComplexity": "high",
33 "dependencies": ["identity-service", "product-service"],
34 "dependents": ["notification-service", "analytics-service"],
35 "dataStores": ["orders table", "order_items table", "cart table"],
36 "apis": [
37 "POST /orders",
38 "GET /orders/:id",
39 "PATCH /orders/:id/status"
40 ]
41 }
42 ],
43 "extractionOrder": [
44 "identity-service", // No dependencies, extract first
45 "product-service", // Only depends on identity
46 "order-service", // Depends on identity and products
47 "notification-service" // Depends on orders
48 ],
49 "warnings": [
50 "orders module has tight coupling to users via 15 direct imports",
51 "Shared utility functions need to be extracted to shared library"
52 ]
53}Phase 3: Strangler Pattern Implementation#
AI helps implement the strangler fig pattern—wrapping the monolith and gradually replacing pieces.
Creating the Facade#
1// AI generates facade layer
2// src/facades/user-facade.ts
3
4import { User } from '@/types';
5import { config } from '@/config';
6
7export class UserFacade {
8 private useNewService = config.features.useNewUserService;
9
10 async getUser(id: string): Promise<User> {
11 if (this.useNewService) {
12 // Call new microservice
13 return this.callNewService(id);
14 } else {
15 // Call monolith
16 return this.callMonolith(id);
17 }
18 }
19
20 private async callNewService(id: string): Promise<User> {
21 const response = await fetch(`${config.userServiceUrl}/users/${id}`);
22 return response.json();
23 }
24
25 private async callMonolith(id: string): Promise<User> {
26 // Import from monolith codebase
27 const { UserService } = await import('@/services/users');
28 return UserService.getById(id);
29 }
30}Feature Flag Configuration#
1// AI generates feature flag setup
2const featureConfig = {
3 useNewUserService: {
4 default: false,
5 rollout: {
6 percentage: 0,
7 enabledFor: ['internal-users', 'beta-testers']
8 }
9 },
10 useNewOrderService: {
11 default: false,
12 rollout: {
13 percentage: 0,
14 blockedBy: ['useNewUserService'] // Must enable users first
15 }
16 }
17};Phase 4: Code Transformation#
AI handles the tedious transformation work:
Extracting Service Code#
1// Before: Monolith code
2// src/services/users.ts
3import { db } from '@/lib/database';
4import { hashPassword } from '@/utils/crypto';
5import { sendEmail } from '@/services/email';
6
7export class UserService {
8 static async create(data: CreateUserInput) {
9 const hashedPassword = await hashPassword(data.password);
10 const user = await db.user.create({
11 data: { ...data, password: hashedPassword }
12 });
13 await sendEmail(user.email, 'welcome');
14 return user;
15 }
16}
17
18// After: AI extracts to microservice
19// user-service/src/user.service.ts
20import { db } from './database';
21import { hashPassword } from './crypto';
22import { publishEvent } from './events';
23
24export class UserService {
25 static async create(data: CreateUserInput) {
26 const hashedPassword = await hashPassword(data.password);
27 const user = await db.user.create({
28 data: { ...data, password: hashedPassword }
29 });
30
31 // Event-driven instead of direct call
32 await publishEvent('user.created', { userId: user.id });
33
34 return user;
35 }
36}Generating API Contracts#
AI creates OpenAPI specs from existing code:
1# Generated by AI from code analysis
2openapi: 3.0.0
3info:
4 title: User Service API
5 version: 1.0.0
6
7paths:
8 /users:
9 post:
10 summary: Create a new user
11 requestBody:
12 required: true
13 content:
14 application/json:
15 schema:
16 $ref: '#/components/schemas/CreateUserInput'
17 responses:
18 '201':
19 description: User created
20 content:
21 application/json:
22 schema:
23 $ref: '#/components/schemas/User'
24
25components:
26 schemas:
27 CreateUserInput:
28 type: object
29 required: [email, password, name]
30 properties:
31 email:
32 type: string
33 format: email
34 password:
35 type: string
36 minLength: 8
37 name:
38 type: string
39 User:
40 type: object
41 properties:
42 id:
43 type: string
44 email:
45 type: string
46 name:
47 type: string
48 createdAt:
49 type: string
50 format: date-timePhase 5: Testing Migration#
AI generates tests to validate the migration:
Contract Tests#
1// AI-generated contract tests
2describe('User Service Contract', () => {
3 describe('GET /users/:id', () => {
4 it('returns user matching monolith response shape', async () => {
5 const userId = 'test-user-id';
6
7 // Get from monolith
8 const monolithResponse = await monolith.get(`/users/${userId}`);
9
10 // Get from microservice
11 const microserviceResponse = await microservice.get(`/users/${userId}`);
12
13 // Compare shapes (ignore timestamps)
14 expect(microserviceResponse.data).toMatchObject({
15 id: monolithResponse.data.id,
16 email: monolithResponse.data.email,
17 name: monolithResponse.data.name
18 });
19 });
20 });
21});Data Consistency Tests#
1// Verify data sync between old and new systems
2describe('Data Consistency', () => {
3 it('maintains user count consistency', async () => {
4 const monolithCount = await monolithDb.user.count();
5 const microserviceCount = await microserviceDb.user.count();
6
7 expect(microserviceCount).toBe(monolithCount);
8 });
9
10 it('maintains data integrity for sample users', async () => {
11 const sampleIds = await getSampleUserIds(100);
12
13 for (const id of sampleIds) {
14 const monolithUser = await monolithDb.user.findUnique({ where: { id }});
15 const microserviceUser = await microserviceDb.user.findUnique({ where: { id }});
16
17 expect(microserviceUser).toMatchObject({
18 id: monolithUser.id,
19 email: monolithUser.email,
20 name: monolithUser.name
21 });
22 }
23 });
24});Phase 6: Gradual Rollout#
AI monitors the rollout and suggests adjustments:
1// AI rollout assistant
2const rolloutAnalysis = await aiAgent.analyzeRollout({
3 service: 'user-service',
4 currentPercentage: 10,
5 metrics: await getMetrics()
6});
7
8// AI output
9{
10 "status": "healthy",
11 "recommendation": "increase_rollout",
12 "suggestedPercentage": 25,
13 "rationale": [
14 "Error rate is 0.01% (below 0.1% threshold)",
15 "P99 latency is 45ms (below 100ms threshold)",
16 "No data consistency alerts in past 24h"
17 ],
18 "watchItems": [
19 "Monitor database connection pool as traffic increases",
20 "Order service latency increased 5ms - likely due to network hop"
21 ]
22}The Human Decisions#
AI handles the grunt work, but humans make the critical decisions:
You Decide#
- Which services to extract first: Based on business priority
- Service boundaries: AI suggests, you validate against domain knowledge
- Rollout speed: Based on risk tolerance
- When to cut over: Based on confidence level
AI Handles#
- Dependency analysis
- Code transformation
- Test generation
- Contract creation
- Monitoring and alerting
Success Metrics#
Track these throughout your migration:
| Metric | Healthy | Warning | Critical |
|---|---|---|---|
| Error rate increase | <0.1% | 0.1-0.5% | >0.5% |
| Latency increase | <10ms | 10-50ms | >50ms |
| Data consistency | 100% | 99.9% | <99.9% |
| Test coverage | >80% | 60-80% | <60% |
Conclusion#
Migrating from monolith to microservices is still hard. AI doesn't change that. But it does change how much time you spend on tedious analysis and transformation versus actual architecture decisions.
Use AI for the grunt work. Keep humans on the strategy.
Bootspring's refactoring agents help teams migrate safely. See how we've helped companies extract services without downtime.