Chapter 5

AI-adapted architecture paradigm

Learn how to design architecture patterns suitable for AI code generation, so AI becomes an aid to the architecture rather than an obstacle.

Core insight: architecture is context

In the era of AI programming,Architecture design needs to account for AI characteristics. A good architecture should make it easier for AI to understand and generate code, rather than adding complexity. This chapter introduces four AI-friendly architectural paradigms.

Clean Architecture with AI

The layered structure of Clean Architecture is naturally suited to AI-based chunked generation and maintenance, with clear responsibility boundaries at each layer.

AI generates interfaces and implementations for each layer

The layered structure of Clean Architecture allows AI to focus on code generation for a single layer.

Entities (entity layer)

AI generates domain entities based on business requirements, including business rules and validation logic

• Pure business logic, no external dependencies
• AI can generate entity code based on the domain model

Use Cases (use case layer)

AI generates use case implementations based on user stories, coordinating the entity and interface layers

• Implement specific business use cases
• AI can generate use-case code based on the PRD

Interfaces (interface layer)

AI generates adapter implementations based on interface specifications, connecting external systems

• Implement external interface adaptation
• AI can generate interface code based on API documentation

Rule-based AI checks

AI can automatically check whether code complies with Clean Architecture dependency rules.

Dependency Rules

The inner layer does not depend on the outer layer; the outer layer depends on the inner layer
Dependency direction can only point inward, not outward
The entity layer does not depend on any other layer

AI review capability

• Analyze code dependencies and detect forbidden dependencies
• Generate dependency diagrams to visualize the architecture
• Propose refactoring suggestions and fix dependency issues

Advantages: loose coupling between layers, suitable for AI chunked generation and maintenance

Context isolation

Each layer has a clear responsibility, and when AI generates code, it only needs to focus on the context of the current layer

Parallel development

Different layers can be generated in parallel, improving development efficiency

Easy to maintain

When modifying one layer, AI only needs to focus on that layer’s code and will not affect other layers

CDD (Component-Driven Development)

Component-Driven Development emphasizes building applications from components, and this bottom-up approach is very well suited to AI generation.

AI-assisted design of atomic components (Atomic Design)

Atomic Design divides components into five layers: atoms, molecules, organisms, templates, and pages, and AI can start generating from the most basic atom components.

Atom

Button, Input

Molecule

SearchBox

Organization

Header, Footer

Template

Page Layout

Page

Complete Page

AI generation workflow

1. Start with atomic components and let AI generate basic UI components

2. Combine atomic components to generate molecular components

3. Combine molecular components to generate organizational components

4. Compose and organize components to generate page templates

5. Generate a complete page based on the template

Automatically generate Storybook documentation and tests

AI can automatically generate Storybook documentation and test cases for each component.

Storybook Documentation

• AI analyzes component Props and generates Story files
• Automatically generate Stories in different states
• Generate component usage documentation
• Generate sample component code

Test case

• AI-generated unit tests
• Generate visual regression tests
• Generate interaction tests
• Generate accessibility tests

Advantage: context isolation, allowing AI to focus on implementing a single component

Component Isolation

Each component is independent, and when generating with AI you only need to focus on the component's Props and functionality

Reusability

The generated components can be reused in multiple places, improving development efficiency

Easy to test

Component-level tests are easier to write and maintain

Spec-First Development

Spec-First Development emphasizes writing the specification first and then generating code, with the specification becoming AI’s "source of truth".

PRD → OpenAPI Spec → Code generation flow

PRD document

Write a product requirements document, defining features and business rules

OpenAPI Spec

Generate OpenAPI specifications based on the PRD and define API interfaces

Code generation

AI generates client and server code based on OpenAPI Spec

Advantage: Use Spec as the AI’s "source of truth"

Single source of truth

OpenAPI Spec is the single source of truth for the API; frontend, backend, and documentation are all generated from the same Spec, ensuring consistency

Type safety

Spec defines complete type information, and AI-generated code is inherently type-safe

Automatic synchronization

When the Spec is updated, AI can automatically update all related code to keep everything in sync

AI generation capabilities

• Generate TypeScript type definitions based on the Spec
• Generate API client code
• Generate server-side routes and controllers
• Generate API documentation and mock data
• Generate integration test cases

Micro-Agent Pattern

The Micro-Agent Pattern breaks complex functionality into multiple specialized agents, with each agent responsible for a specific task.

Break complex features into a multi-Agent collaboration network

Break a large task into multiple subtasks, with each subtask handled by a dedicated Agent, and Agents collaborating through message passing.

Agent Types

Architect Agent

Responsible for system design and architecture decisions

Domain expert Agent

Responsible for business logic and domain knowledge

Review Agent

Responsible for code review and quality checks

Collaboration process

1. The architect agent analyzes the requirements and develops an architecture plan

2. Domain expert Agent implements business logic

3. Review Agent checks code quality and suggests improvements

4. Agents collaborate through message passing to complete complex tasks

Architect Agent + Domain Expert Agent + Review Agent

Architect Agent

• Analyze system requirements and design the overall architecture
• Choose the right technology stack and design patterns
• Define module boundaries and interface specifications
• Develop a development plan and break down tasks

Domain expert Agent

• Understand business requirements and implement business logic
• Apply domain-driven design principles
• Generate domain models and business code
• Ensure code complies with business rules

Review Agent

• Check code quality and standards
• Identify potential issues and risks
• Provide refactoring and optimization suggestions
• Ensure the code conforms to the architecture design

Advantage: specialized division of labor improves code quality

Specialization

Each Agent focuses on its own domain, improving expertise and accuracy

Scalable

Can add new Agents as needed to extend the system's capabilities

Quality Assurance

Review the Agent to ensure code quality and reduce errors and defects

Learning outcomes

After completing this chapter, you will:

1Understand how Clean Architecture adapts to AI code generation and master layered code generation strategies
2Master the workflow of Component-Driven Development and use AI to assist component design
3Understand the value of Spec-First Development and be able to establish a specification-driven development process
4Master the design philosophy of the Micro-Agent Pattern and be able to design multi-agent collaboration systems
5Able to choose the appropriate AI adaptation architecture paradigm based on project characteristics

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