📏 Coding Standards for AI Agents: Best Practices

📐 Architecture Diagram

graph TD A[Agent Coding Standards] --> B[Architecture] A --> C[Error Handling] A --> D[Testing] A --> E[Security] A --> F[Observability] B --> B1[Modular Tool Design] B --> B2[State Management] C --> C1[Graceful Degradation] C --> C2[Retry with Backoff] D --> D1[Unit Tests for Tools] D --> D2[Integration Tests] D --> D3[Evaluation Suites] E --> E1[Input Sanitization] E --> E2[Permission Boundaries] F --> F1[Structured Logging] F --> F2[Tracing - LangSmith] style A fill:#6C63FF,color:#fff style D fill:#FF6584,color:#fff style E fill:#00C9A7,color:#fff

Building AI agents is exciting, but without proper engineering practices, they become unreliable, insecure, and unmaintainable. Here are the coding standards every AI engineer should follow.

🏗️ Architecture Principles

Modular Tools: Each tool should do ONE thing well — don't build god-tools
Stateless Functions: Tools should be pure functions where possible
State Management: Use explicit state objects, not global variables
Separation of Concerns: Keep LLM logic, tool logic, and orchestration separate

# ✅ Good: Single-responsibility tool
def search_database(query: str, limit: int = 10) -> list[dict]:
    '''Search products database. Returns list of matching products.'''
    return db.products.search(query, limit=limit)

# ❌ Bad: God-tool that does everything
def handle_request(request: str) -> str:
    # searches, processes, formats, sends email...

⚠️ Error Handling

Never let agents crash silently: Always return structured error messages
Retry with exponential backoff: API calls to LLMs will fail
Timeout enforcement: Set max execution time for agent loops
Graceful degradation: If a tool fails, the agent should adapt, not crash

🧪 Testing Strategy

Unit tests: Test each tool independently
Integration tests: Test tool + LLM interactions with mocked responses
Evaluation suites: Run agents against test scenarios and measure success rate
Regression tests: Ensure prompt changes don't break existing behavior

🔒 Security

Input Sanitization: Never pass raw user input to system commands
Permission Boundaries: Limit what tools can access (read-only DB, no admin APIs)
Prompt Injection Defense: Separate user input from system instructions
Output Filtering: Prevent sensitive data from leaking in responses

📊 Observability

Structured Logging: Log every agent step with timestamps, tool calls, and results
Tracing: Use LangSmith, Arize, or custom tracing for end-to-end visibility
Metrics: Track latency, token usage, success rate, and cost per agent run

#AIAgents #CodingStandards #SoftwareEngineering #BestPractices #CleanCode

📏 Coding Standards for AI Agents: Best Practices