Chapter 4

AI-native development mode

Master the development paradigms of the AI era, from TDD 2.0 to Agent-Driven Development, and make AI your best development partner.

Core insight: AI-native thinking

In the era of AI programming,Development mode requires redesign. Traditional development methods need to adapt to AI capabilities and characteristics in order to realize their maximum value. This chapter will introduce four AI-native development modes to help you build a new development mindset.

TDD (Test-Driven Development) 2.0

Traditional TDD is a red-green-refactor cycle, and the AI era makes this cycle faster and smarter.

AI-generated test cases (Given-When-Then)

Use BDD (Behavior-Driven Development) style test cases to make it easier for AI to understand requirements and generate tests.

Given-When-Then format
Given User is logged in
When The user clicked the "Submit Order" button
Then The system creates an order and returns the order number
AI advantages
  • AI can quickly generate a large number of test cases, covering edge cases
  • The Given-When-Then format makes it easier for AI to understand test intent
  • Test cases are documentation, and AI can generate implementation code based on tests

AI acceleration mechanism for the red-green-refactor cycle

Red

Write tests

AI generates test cases based on requirements, quickly covering feature points

Green

Write the implementation

AI generates a minimal viable implementation based on test cases, quickly passing the tests

Refactoring

Optimize code

AI analyzes code quality, proposes refactoring suggestions, and performs optimization

Hands-on: Have AI write tests first, then implementation

Step 1: Describe requirements
"Implement a user login feature that requires validating the username and password"
Step 2: AI generates test cases
AI generates Given-When-Then format test cases based on requirements
Step 3: AI generates implementation code
AI generates a minimal viable implementation based on test cases to ensure tests pass
Step 4: AI refactoring optimization
AI analyzes code quality, proposes refactoring suggestions, and executes them

DDD (Domain-Driven Design) with AI

Domain-driven design requires a deep understanding of the business domain, and AI can help us distill domain knowledge more effectively.

Use AI to extract a ubiquitous language

A ubiquitous language is at the core of DDD, and AI can help us extract domain terms from business documents.

AI-assisted workflow
  • 1.Input business documents, requirement documents, user stories
  • 2.AI analyzes documents and extracts domain terms and concepts
  • 3.AI recognizes synonyms and related relationships of terms
  • 4.Generate a unified language dictionary to ensure consistent terminology across the team

AI-assisted partitioning of bounded contexts (Bounded Contexts)

Bounded Context is the most important concept in DDD, and AI can help identify and divide context boundaries.

Identify context
  • • AI analyzes business domains and identifies different business contexts
  • • Identify dependencies between contexts
  • • Identify context mapping patterns (shared kernel, customer-supplier, etc.)
Divide boundaries
  • • AI suggests a context boundary partitioning plan
  • • Identify integration points between contexts
  • • Generate a context map

Code generation strategy for entities/value objects/aggregate roots

AI can automatically generate the code structure for entities, value objects, and aggregate roots based on the domain model.

Entity (Entity)

AI identifies business objects with unique identifiers and generates entity classes and ID properties

Value Object

AI identifies immutable value types and generates value object classes and validation logic

Aggregate Root

AI identifies aggregate boundaries and generates aggregate root classes and business rules

ADD (Agent-Driven Development)

Agent-Driven Development is a new development paradigm in which multiple AI agents collaborate to complete development tasks.

Plan-Execute-Evaluate loop

Plan

Planning stage

The architect agent analyzes requirements, creates a development plan, and breaks down tasks

Execute

Execution phase

The coding agent generates code based on the plan, and the testing agent generates test cases

Evaluate

Evaluation phase

Review Agent checks code quality, makes improvement suggestions, and optimizes iteratively

Multi-agent collaboration pattern

Architect Agent
  • • Analyze requirements
  • • Design the architecture
  • • Make a plan
Coding Agent
  • • Generate code
  • • Implement features
  • • Code optimization
Test Agent
  • • Generate tests
  • • Execute tests
  • • Quality check

RAG-DD (RAG-Driven Development)

RAG-Driven Development uses a private knowledge base to enhance AI’s code generation capabilities, enabling AI to generate code based on the team’s knowledge and experience.

Code generation based on a private knowledge base

Turn the team’s architecture documents, design patterns, best practices, and other knowledge into a knowledge base so AI can refer to this knowledge when generating code.

Knowledge base content
  • Architecture design documents and design patterns
  • Code conventions and best practices
  • Past project experience and lessons learned
  • The team's tech stack and toolchain
Workflow
1. The user presents the requirements
2. Retrieve relevant knowledge in the RAG system
3. AI generates code based on retrieved knowledge
4. The generated code complies with the team’s architecture and standards

Architecture documents are context

Use architecture documents as the core context of the RAG system to ensure the generated code conforms to the architecture design.

Architecture document types
  • • System architecture diagrams and technical selection documents
  • • API design standards and interface documentation
  • • Database design and data models
  • • Microservice decomposition and service boundaries
  • • Security standards and permission model
Advantages
  • The generated code automatically conforms to the architecture design
  • Reduce architectural drift and refactoring costs
  • New members can quickly understand the architecture and generate code that follows standards

Learning outcomes

After completing this chapter, you will:

  • 1Understand the core ideas of TDD 2.0 and use AI to accelerate test-driven development
  • 2Understand how DDD with AI works and be able to use AI to assist domain modeling
  • 3Master the multi-agent collaboration pattern of Agent-Driven Development
  • 4Understand the value of RAG-Driven Development and be able to build a private knowledge base
  • 5Able to choose the appropriate AI-native development mode based on project characteristics
Previous chapter: PRD and document-drivenNext chapter: AI-adaptive architecture paradigms