AI-Driven Architecture Decisions

The Architecture Challenge

Complex projects require thoughtful architectural decisions that impact:

• Scalability and performance
• Developer experience
• Maintenance burden
• Time to market

AI assistants can accelerate these decisions by analyzing patterns, suggesting trade-offs, and helping you explore options systematically.

Using AI for Architecture Analysis

Pattern Recognition

claude "Analyze this codebase structure and identify:
1. Current architectural patterns in use
2. Inconsistencies or anti-patterns
3. Opportunities for improvement
4. Technical debt hotspots"

Claude Code will examine your project and provide insights:

## Architecture Analysis

### Current Patterns
- **Component Architecture**: React with feature-based folders
- **State Management**: Redux Toolkit with slices pattern
- **API Layer**: REST with custom fetch wrapper
- **Styling**: Tailwind CSS with component-level styles

### Identified Issues
1. Mixed data fetching approaches (useEffect vs React Query)
2. Inconsistent error handling across API calls
3. Business logic scattered in components vs services
4. Missing domain layer abstraction

### Recommendations
1. Standardize on React Query for server state
2. Create centralized error boundary system
3. Extract business logic to domain services
4. Consider module federation for scaling

Framework and Library Selection

The Decision Matrix Approach

claude "I need to add real-time features to this Next.js app.
Compare these options for our use case:
1. Socket.io
2. Pusher
3. Ably
4. Supabase Realtime

Consider: our current stack, team size (3 devs),
expected scale (10k concurrent users),
and budget constraints."

AI provides structured analysis:

Criteria	Socket.io	Pusher	Ably	Supabase
Integration	Moderate	Easy	Easy	Easiest
Scalability	Self-managed	Managed	Managed	Managed
Cost at 10k	Server costs	$$$$	$$$	$$
Learning curve	Medium	Low	Low	Low
Our stack fit	Good	Good	Good	Best

Recommendation: Supabase Realtime—already using Supabase for auth, minimal integration overhead, cost-effective at target scale.

Monorepo vs Polyrepo Decisions

Analyzing Current State

claude "We have 3 separate repos:
- frontend (Next.js)
- backend-api (Node/Express)
- shared-types (TypeScript types)

Analyze if consolidating to a monorepo makes sense.
Consider: CI/CD complexity, type sharing, deployment needs."

AI-Guided Migration Planning

claude "Create a migration plan from our current polyrepo
to a Turborepo monorepo. Include:
1. Directory structure
2. Shared package configuration
3. CI/CD pipeline updates
4. Risk mitigation strategies"

Microservices vs Monolith

When AI Recommends Staying Monolithic

User: "Should we split our app into microservices?"

Claude: "Based on my analysis of your codebase:

**Current State:**
- 15k lines of code
- 3-person team
- Single deployment target
- Tightly coupled domain logic

**Recommendation: Stay Monolithic**

Reasons:
1. Team size doesn't support microservices overhead
2. Domain boundaries aren't clear yet
3. No scaling bottlenecks identified
4. Would introduce network complexity prematurely

**Alternative: Modular Monolith**
Structure code into clear modules that COULD become
services later, but deploy as single unit now.

Architecture Documentation with AI

Generating C4 Diagrams

claude "Generate a C4 Context diagram for our system.
Include:
- Our Next.js frontend
- Node.js API
- PostgreSQL database
- Auth0 for authentication
- Stripe for payments
- SendGrid for emails"

AI generates PlantUML or Mermaid:

C4Context
    title System Context Diagram

    Person(user, "End User", "Uses the application")

    System(webapp, "Web Application", "Next.js frontend")
    System(api, "API Server", "Node.js backend")

    SystemDb(db, "Database", "PostgreSQL")

    System_Ext(auth0, "Auth0", "Authentication")
    System_Ext(stripe, "Stripe", "Payments")
    System_Ext(sendgrid, "SendGrid", "Email")

    Rel(user, webapp, "Uses", "HTTPS")
    Rel(webapp, api, "API calls", "HTTPS")
    Rel(api, db, "Reads/Writes")
    Rel(api, auth0, "Authenticates")
    Rel(api, stripe, "Processes payments")
    Rel(api, sendgrid, "Sends emails")

Decision Records with AI

Generating ADRs

claude "Create an ADR (Architecture Decision Record) for
choosing React Query over Redux for server state.
Follow the standard ADR template."

AI generates structured ADR:

# ADR-003: React Query for Server State

## Status
Accepted

## Context
Our application mixes local UI state and server-cached data
in Redux, leading to complex synchronization logic and
stale data issues.

## Decision
Adopt React Query for all server state management while
keeping Redux Toolkit for client-only UI state.

## Consequences

### Positive
- Automatic cache invalidation
- Built-in loading/error states
- Reduced boilerplate
- Better separation of concerns

### Negative
- Learning curve for team
- Two state management libraries
- Migration effort required

### Neutral
- Bundle size similar
- Testing patterns change

Best Practices for AI Architecture Consultation

1. Provide Full Context: Share constraints, team size, scale requirements
2. Ask for Trade-offs: Request pros/cons, not just recommendations
3. Validate Assumptions: Have AI explain reasoning
4. Iterate on Options: Explore multiple paths before deciding
5. Document Decisions: Use AI to generate ADRs for future reference

Next Lesson

We'll apply these principles to build a full-stack feature across a monorepo structure. :::