Software Architect
Microservices Architecture Design — Split the Monolith Without the Chaos
Key Takeaway
The Microservices skill designs service decomposition, API contracts, data ownership boundaries, communication patterns (sync vs async), and deployment topology. Your agent prevents the #1 microservices mistake: creating a distributed monolith.
The Problem
"We should move to microservices." This statement has launched more failed projects than any other architectural decision.
The promise: independent deployments, team autonomy, technology diversity, better scaling.
The reality (for most teams): distributed monolith, 10x operational complexity, cascading failures, network debugging nightmares, and a 6-month migration that took 2 years.
Microservices fail when:
- Wrong service boundaries: Split by technical layer (API service, DB service) instead of business domain
- Shared databases: Two services reading/writing the same tables = coupled forever
- Synchronous chains: Service A calls B calls C calls D = one failure takes down everything
- No service mesh: No observability, no circuit breakers, no retry policies
- Premature decomposition: 3 engineers splitting into 12 microservices = more overhead than value
The Solution
The Microservices skill designs the decomposition using Domain-Driven Design principles — bounded contexts, aggregate roots, domain events, and anti-corruption layers. It tells you what to split AND what to keep together.
The Process
View details
You: Our e-commerce monolith is becoming unmanageable.
Team of 15 engineers. Features: product catalog, inventory,
orders, payments, shipping, user accounts, reviews,
recommendations, notifications.
Design the microservices architecture.
The agent produces:
View details
## Service Decomposition — E-Commerce Platform
### RECOMMENDED: 6 Services (NOT 9)
Why not 9? Three of your "domains" are tightly coupled
and should stay together:
1. ✅ CATALOG SERVICE (Product + Reviews + Recommendations)
Reason: Reviews and recommendations are meaningless
without products. Same team. Same data access patterns.
Splitting creates unnecessary network calls for every
product page load.
2. ✅ ORDER SERVICE (Orders + Inventory)
Reason: Order placement and inventory decrement MUST
be atomic. Separating them creates distributed
transaction nightmares (two-phase commit, saga pattern
for what should be a single DB transaction).
3. ✅ PAYMENT SERVICE
Reason: Genuinely independent. Different compliance
requirements (PCI). Different change velocity.
4. ✅ SHIPPING SERVICE
Reason: Different integration patterns (carrier APIs).
Can change independently from order logic.
5. ✅ USER SERVICE (Accounts + Auth)
Reason: Standard bounded context. Token issuance,
profile management, preferences.
6. ✅ NOTIFICATION SERVICE
Reason: Cross-cutting concern. Email, SMS, push.
Should be async, event-driven.
### COMMUNICATION PATTERNS
Synchronous (REST/gRPC):
- Frontend → Catalog: product queries (low latency required)
- Frontend → Order: place order (user is waiting)
- Order → Payment: charge card (synchronous, user waiting)
Asynchronous (events via Kafka/RabbitMQ):
- Order → Shipping: "OrderPlaced" event (no user waiting)
- Order → Notification: "OrderConfirmed" event
- Payment → Notification: "PaymentFailed" event
- Catalog → Recommendation: "ProductViewed" event
### DATA OWNERSHIP (the most important diagram)
Each service owns its data. Period.
| Service | Database | Shared? |
|---|---|---|
| Catalog | catalog_db (Postgres) | NO |
| Order | order_db (Postgres) | NO |
| Payment | payment_db (Postgres, encrypted) | NO |
| Shipping | shipping_db (Postgres) | NO |
| User | user_db (Postgres) | NO |
| Notification | notification_db (Redis/Postgres) | NO |
If Service A needs data from Service B: API call or
event subscription. NEVER direct database access.
### WHAT NOT TO DO
❌ "API Gateway Service" — use an actual API gateway (Kong, AWS API Gateway)
❌ "Database Service" — this is a shared database with extra steps
❌ "Common Library" with business logic — that's a distributed monolith
❌ "Orchestration Service" that coordinates everything — single point of failure
The Results
| Metric | "Split Everything" | AI-Designed Architecture |
|---|---|---|
| Services created | 9-12 (too many) | 6 (right-sized) |
| Distributed transactions | Many (complex) | Minimized (co-located related logic) |
| Team mapping | 1 engineer per service (understaffed) | 2-3 engineers per service (sustainable) |
| Operational complexity | Overwhelming | Manageable |
| Data integrity | Eventual consistency everywhere | Transactional where needed |
| Migration time | 18-24 months | 6-9 months |
Setup on MrChief
yamlShow code
skills:
- microservices
- api-design # For service contracts
- database-design # For data ownership boundaries
- docker # For containerization
- kubernetes # For orchestration
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