System Design for AI Agents

System Design for AI Agents

Building reliable, maintainable agent systems requires solid engineering practices.

Modular Architecture

Separate concerns into independent modules:

class AgentSystem:
    def __init__(self):
        self.perception = PerceptionModule()   # Parse inputs
        self.memory = MemoryModule()           # Store/retrieve context
        self.reasoning = ReasoningModule()     # Decide actions
        self.action = ActionModule()           # Execute actions
        self.monitoring = MonitoringModule()   # Track performance

Error Handling and Retry Logic

async def execute_with_retry(action, max_retries=3):
    for attempt in range(max_retries):
        try:
            result = await action()
            return result
        except RateLimitError:
            await asyncio.sleep(2 ** attempt)  # Exponential backoff
        except ToolError as e:
            if attempt == max_retries - 1:
                return fallback_response(e)
            continue

Logging and Observability

You can't fix what you can't see:

• Log every LLM call (prompt, response, tokens, latency)
• Track tool calls and their results
• Monitor error rates and types
• Record user feedback
• Use structured logging (JSON) for easy analysis

Cost Management

• Token counting: Track input/output tokens per request
• Caching: Cache repeated queries and embeddings
• Model routing: Use cheaper models for simple tasks
• Rate limiting: Prevent abuse and runaway costs
• Budget alerts: Notify when spending exceeds thresholds

Key Takeaways

• Modular design makes agents easier to test, debug, and maintain
• Robust error handling prevents cascading failures
• Observability is essential — log everything
• Cost management prevents surprise bills
• Design for failure — agents will encounter unexpected situations