AI Code Generator

Overview

This workflow automates the code generation process from natural language specifications to production-ready code with testing and documentation. It leverages multiple specialized agents to ensure high-quality output.

Difficulty

Medium - Requires understanding of agent orchestration and code quality principles.

Tools Required

• OpenAI Agents SDK / Claude: Primary LLM for code generation and reasoning
• GitHub: Repository management and version control
• GitHub Copilot: AI-powered code suggestions
• Sentry: Error tracking for generated code validation

Workflow Steps

Step 1: Requirements Agent

The Requirements Agent clarifies and expands vague specifications into detailed technical requirements.

# Example prompt
"""
You are a Requirements Agent. Your task is to clarify and expand user specifications.

User request: "Create a REST API for a todo app"

Generate:
1. Functional requirements (CRUD operations, authentication, etc.)
2. Non-functional requirements (performance, security, scalability)
3. Technology stack recommendations
4. API endpoint specifications
5. Data models and schemas
"""

Step 2: Architect Agent

The Architect Agent designs the solution structure based on requirements.

# Example output
"""
Architecture Design:

Project Structure:
├── src/
│   ├── api/
│   │   ├── routes/
│   │   ├── middleware/
│   │   └── validators/
│   ├── models/
│   ├── services/
│   └── utils/
├── tests/
├── docker/
└── docs/

Key Decisions:
- Framework: FastAPI (async support, automatic docs)
- Database: PostgreSQL with SQLAlchemy ORM
- Auth: JWT tokens with refresh mechanism
- Testing: pytest with pytest-asyncio
"""

Step 3: Coder Agent

The Coder Agent writes implementation code following the architecture.

# Example: models/todo.py
from sqlalchemy import Column, Integer, String, Boolean, DateTime
from sqlalchemy.orm import declarative_base
from datetime import datetime

Base = declarative_base()

class Todo(Base):
    __tablename__ = "todos"
    
    id = Column(Integer, primary_key=True, index=True)
    title = Column(String, nullable=False)
    description = Column(String, nullable=True)
    completed = Column(Boolean, default=False)
    created_at = Column(DateTime, default=datetime.utcnow)
    updated_at = Column(DateTime, default=datetime.utcnow, onupdate=datetime.utcnow)

Step 4: Tester Agent

The Tester Agent generates and runs unit tests.

# tests/test_api.py
import pytest
from fastapi.testclient import TestClient
from src.main import app

client = TestClient(app)

def test_create_todo():
    response = client.post("/todos", json={
        "title": "Test todo",
        "description": "Test description"
    })
    assert response.status_code == 201
    data = response.json()
    assert data["title"] == "Test todo"
    assert data["completed"] == False

def test_get_todo():
    response = client.get("/todos/1")
    assert response.status_code == 200

Step 5: Doc Agent

The Doc Agent creates documentation and README.

# Todo API

A REST API for managing todos.

## Endpoints

### Create Todo
`POST /todos`

### Get Todo
`GET /todos/{id}`

### Update Todo
`PUT /todos/{id}`

### Delete Todo
`DELETE /todos/{id}`

## Running

```bash
uvicorn src.main:app --reload

### Step 6: Reviewer Agent

The Reviewer Agent performs code quality checks.

```python
# Quality checklist
"""
Code Review Checklist:

✅ Security:
   - Input validation present
   - SQL injection protection
   - Authentication required where needed
   - No hardcoded secrets

✅ Performance:
   - Database queries optimized
   - No N+1 query problems
   - Proper indexing

✅ Maintainability:
   - Clear naming conventions
   - Single responsibility functions
   - Adequate test coverage (>80%)
   - Documentation complete

✅ Best Practices:
   - Error handling implemented
   - Logging configured
   - Environment variables used
"""

Example Usage

# User request
"Create a Flask API for managing a book collection with CRUD operations"

# Workflow execution
1. Requirements Agent → 15 detailed requirements
2. Architect Agent → Project structure and tech stack
3. Coder Agent → 200+ lines of production code
4. Tester Agent → 50+ test cases
5. Doc Agent → Complete README and API docs
6. Reviewer Agent → Quality report with 95/100 score

Pros

• ✅ Produces production-ready code with tests
• ✅ Consistent code quality across generated code
• ✅ Complete documentation included
• ✅ Reduces boilerplate development time
• ✅ Enforces best practices automatically

Cons

• ❌ May generate overly complex solutions for simple tasks
• ❌ Requires careful prompt engineering for best results
• ❌ Generated code may need human review for edge cases
• ❌ Higher LLM token costs for full workflow

When to Use

Use this workflow when:

• You need to generate boilerplate code quickly
• You're building a new feature from scratch
• You need consistent code quality across a team
• You want automated documentation

Consider alternatives when:

• You're doing quick prototypes (use simpler single-agent approach)
• You need highly specialized domain logic (use human developers)
• You're working on performance-critical code (manual optimization needed)

Resources

• OpenAI Agents SDK
• Claude Code
• GitHub Copilot