architecture-patterns – Coding skill for AI agents

Architecture Patterns

Master proven backend architecture patterns including Clean Architecture, Hexagonal Architecture, and Domain-Driven Design to build maintainable, testable, and scalable systems.

Given: a service boundary or module to architect.

Produces: layered structure with clear dependency rules, interface definitions, and test boundaries.

When to Use This Skill

• Designing new backend services or microservices from scratch
• Refactoring monolithic applications where business logic is entangled with ORM models or HTTP concerns
• Establishing bounded contexts before splitting a system into services
• Debugging dependency cycles where infrastructure code bleeds into the domain layer
• Creating testable codebases where use-case tests do not require a running database
• Implementing domain-driven design tactical patterns (aggregates, value objects, domain events)

Core Concepts

1. Clean Architecture (Uncle Bob)

Layers (dependency flows inward):

• Entities: Core business models, no framework imports
• Use Cases: Application business rules, orchestrate entities
• Interface Adapters: Controllers, presenters, gateways — translate between use cases and external formats
• Frameworks & Drivers: UI, database, external services — all at the outermost ring

Key Principles:

• Dependencies point inward only; inner layers know nothing about outer layers
• Business logic is independent of frameworks, databases, and delivery mechanisms
• Every layer boundary is crossed via an abstract interface
• Testable without UI, database, or external services

2. Hexagonal Architecture (Ports and Adapters)

Components:

• Domain Core: Business logic lives here, framework-free
• Ports: Abstract interfaces that define how the core interacts with the outside world (driving and driven)
• Adapters: Concrete implementations of ports (PostgreSQL adapter, Stripe adapter, REST adapter)

Benefits:

• Swap implementations without touching the core (e.g., replace PostgreSQL with DynamoDB)
• Use in-memory adapters in tests — no Docker required
• Technology decisions deferred to the edges

3. Domain-Driven Design (DDD)

Strategic Patterns:

• Bounded Contexts: Isolate a coherent model for one subdomain; avoid sharing a single model across the whole system
• Context Mapping: Define how contexts relate (Anti-Corruption Layer, Shared Kernel, Open Host Service)
• Ubiquitous Language: Every term in code matches the term used by domain experts

Tactical Patterns:

• Entities: Objects with stable identity that change over time
• Value Objects: Immutable objects identified by their attributes (Email, Money, Address)
• Aggregates: Consistency boundaries; only the root is accessible from outside
• Repositories: Persist and reconstitute aggregates; abstract over the storage mechanism
• Domain Events: Capture things that happened inside the domain; used for cross-aggregate coordination

Detailed patterns and worked examples

Detailed pattern documentation lives in references/details.md. Read that file when the navigation tier above is insufficient.

Testing — In-Memory Adapters

The hallmark of correctly applied Clean Architecture is that every use case can be exercised in a plain unit test with no real database, no Docker, and no network:

# tests/unit/test_create_user.py
import asyncio
from typing import Dict, Optional
from domain.entities.user import User
from domain.interfaces.user_repository import IUserRepository
from use_cases.create_user import CreateUserUseCase, CreateUserRequest


class InMemoryUserRepository(IUserRepository):
    def __init__(self):
        self._store: Dict[str, User] = {}

    async def find_by_id(self, user_id: str) -> Optional[User]:
        return self._store.get(user_id)

    async def find_by_email(self, email: str) -> Optional[User]:
        return next((u for u in self._store.values() if u.email == email), None)

    async def save(self, user: User) -> User:
        self._store[user.id] = user
        return user

    async def delete(self, user_id: str) -> bool:
        return self._store.pop(user_id, None) is not None


async def test_create_user_succeeds():
    repo = InMemoryUserRepository()
    use_case = CreateUserUseCase(user_repository=repo)

    response = await use_case.execute(CreateUserRequest(email="[email protected]", name="Alice"))

    assert response.success
    assert response.user.email == "[email protected]"
    assert response.user.id is not None


async def test_duplicate_email_rejected():
    repo = InMemoryUserRepository()
    use_case = CreateUserUseCase(user_repository=repo)

    await use_case.execute(CreateUserRequest(email="[email protected]", name="Alice"))
    response = await use_case.execute(CreateUserRequest(email="[email protected]", name="Alice2"))

    assert not response.success
    assert "already exists" in response.error

Troubleshooting

Use case tests require a running database

Business logic has leaked into the infrastructure layer. Move all database calls behind an IRepository interface and inject an in-memory implementation in tests (see Testing section above). The use case constructor must accept the abstract port, not the concrete class.

Circular imports between layers

A common symptom is ImportError: cannot import name X between use_cases and adapters. This happens when a use case imports a concrete adapter class instead of the abstract port. Enforce the rule: use_cases/ imports only from domain/ (entities and interfaces). It must never import from adapters/ or infrastructure/.

Framework decorators appearing in domain entities

If SQLAlchemy Column() or Pydantic Field() annotations appear on domain entities, the entity is no longer pure. Create a separate ORM model in adapters/repositories/ and map to/from the domain entity in the repository's _to_entity() method.

All logic ending up in controllers

When the controller grows beyond HTTP parsing and response formatting, extract the logic into a use case class. A controller method should do three things only: parse the request, call a use case, map the response.

Value objects raising errors too late

Validate invariants in __post_init__ (Python) or the constructor so an invalid Email or Money cannot be constructed at all. This surfaces bad data at the boundary, not deep inside business logic.

Context bleed across bounded contexts

If the Order context is importing User entities from the Identity context, introduce an Anti-Corruption Layer. The Order context should hold its own lightweight CustomerId value object and only call the Identity context through an explicit interface.

Advanced Patterns

For detailed DDD bounded context mapping, full multi-service project trees, Anti-Corruption Layer implementations, and Onion Architecture comparisons, see:

• [references/advanced-patterns.md](references/advanced-patterns.md)

Related Skills

• microservices-patterns — Apply these architecture patterns when decomposing a monolith into services
• cqrs-implementation — Use Clean Architecture as the structural foundation for CQRS command/query separation
• saga-orchestration — Sagas require well-defined aggregate boundaries, which DDD tactical patterns provide
• event-store-design — Domain events produced by aggregates feed directly into an event store