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Code-Writing Agents

Create agents that write, debug, and optimize code autonomously

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Building Your Coding Agent

A production coding agent needs four components: orchestrator (workflow), codebase analyzer (context), code generator (LLM integration), and CI/CD pipeline (automation). Here's production-ready code.

Interactive: Code Explorer

Explore each component:

🎯
Agent Orchestrator
Coordinates the coding workflow from task to deployment
class CodingAgentOrchestrator:
    """Orchestrate autonomous coding workflow"""
    
    def __init__(self, codebase_path: str, llm: LLM):
        self.codebase = CodebaseAnalyzer(codebase_path)
        self.llm = llm
        self.planner = TaskPlanner(llm)
        self.coder = CodeGenerator(llm)
        self.tester = TestRunner()
        self.reviewer = CodeReviewer()
        
    def handle_task(self, task: str) -> TaskResult:
        """Execute coding task from spec to validated code"""
        
        # 1. Understand context
        context = self.codebase.analyze_relevant_code(task)
        
        # 2. Plan approach
        plan = self.planner.create_plan(task, context)
        print(f"Plan: {plan.steps}")
        
        # 3. Generate code
        changes = []
        for step in plan.steps:
            code = self.coder.generate(step, context)
            changes.append(code)
            context.update(code)  # Update context with new code
        
        # 4. Run tests
        test_results = self.tester.run_all_tests()
        
        # 5. Iterate if tests fail
        max_iterations = 3
        for i in range(max_iterations):
            if test_results.all_passed:
                break
            
            # Agent debugs and fixes
            fixes = self.coder.fix_failures(test_results, context)
            changes.extend(fixes)
            test_results = self.tester.run_all_tests()
        
        # 6. Human review
        review = self.reviewer.request_review(changes)
        
        return TaskResult(
            changes=changes,
            tests_passed=test_results.all_passed,
            review_status=review.status,
            iterations=i + 1
        )
    
    def auto_fix_bug(self, error_trace: str) -> BugFix:
        """Autonomous bug fixing"""
        # Analyze error
        root_cause = self.codebase.find_bug_location(error_trace)
        
        # Generate fix
        fix = self.coder.generate_fix(root_cause)
        
        # Add regression test
        test = self.coder.generate_test(root_cause, fix)
        
        # Validate
        self.tester.run_tests([test])
        
        return BugFix(fix=fix, test=test)

Complete Usage Example

# Initialize agent
agent = CodingAgentOrchestrator(
    codebase_path="./src",
    llm=OpenAI(model="gpt-4", temperature=0.2)
)

# Feature implementation
result = agent.handle_task("""
Add password reset functionality:
- Send reset email with token
- Token expires in 1 hour
- Log all reset attempts
- Add tests
""")

print(f"Generated {len(result.changes)} file changes")
print(f"Tests: {'✅ Passed' if result.tests_passed else '❌ Failed'}")
print(f"Iterations: {result.iterations}")

# Bug fixing
bug_fix = agent.auto_fix_bug("""
Traceback (most recent call last):
  File "api.py", line 45, in process_payment
    total = sum([item.price for item in cart.items])
TypeError: unsupported operand type(s) for +: 'int' and 'NoneType'
""")

print(f"Fix applied to {bug_fix.fix.file}")
print(f"Regression test added: {bug_fix.test}")

# CI/CD integration
pipeline = CodingAgentPipeline(agent)

# Auto-implement GitHub issue
pipeline.auto_implement_feature(
    "https://github.com/org/repo/issues/123"
)

# Auto-fix production bug
pipeline.auto_fix_production_bug(error_log)
💡
Deployment Strategy

Week 1: Agent generates code, human reviews 100%.
Week 2-4: Agent handles simple tasks autonomously (tests, docs, small fixes).
Month 2: Agent implements features with human review.
Month 3+: Agent handles 50% of issues end-to-end. Human focuses on architecture and complex problems.

Success metric: 10x productivity increase on routine tasks, 90%+ test coverage, human time freed for high-value work.

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