Stage 12 — Production Agentic Systems

Production Agentic Systems  ·  Comprehensive Technical Training  ·  ⏱ 12–16 hours

Learning Objectives

By the end of this stage you will be able to:

• Design and deploy production-grade agentic systems
• Implement monitoring, logging, and observability
• Handle errors, fallbacks, and graceful degradation
• Choose the right framework for your use case
• Build real-world multi-agent applications
• Evaluate and iterate on agent systems

Section 1: Production Agentic Architecture

Production systems require reliability, observability, and error handling beyond basic agent code.

Key Components

• Agent orchestration: Managing agent lifecycle and communication
• Monitoring: Logging, metrics, traces for debugging
• Error handling: Retries, fallbacks, circuit breakers
• State management: Persistence across failures
• Audit trail: Recording decisions for compliance

Production Skeleton

import logging
from typing import Optional

logger = logging.getLogger(__name__)

class ProductionAgent:
    def __init__(self, agent_id: str, tools: list, llm: str = "gpt-4o"):
        self.agent_id = agent_id
        self.tools = tools
        self.llm = llm
        self.execution_history = []
        self.error_count = 0
        self.max_errors = 3

    async def execute_task(self, task: str, timeout: int = 300):
        """Execute task with monitoring and error handling"""
        start_time = time.time()

        try:
            logger.info(f"Agent {self.agent_id} starting task: {task}")

            # Execute with timeout
            result = await asyncio.wait_for(
                self._run_agent(task),
                timeout=timeout,
            )

            elapsed = time.time() - start_time
            logger.info(f"Task completed in {elapsed}s")

            return {"status": "success", "result": result, "elapsed": elapsed}

        except asyncio.TimeoutError:
            self.error_count += 1
            logger.error(f"Task timeout after {timeout}s")
            return {"status": "timeout", "error": "Exceeded time limit"}

        except Exception as e:
            self.error_count += 1
            logger.error(f"Task failed: {e}")

            if self.error_count >= self.max_errors:
                logger.critical(f"Agent {self.agent_id} exceeded error limit. Disabling.")
                # Notify operations team
                alert_ops(f"Agent {self.agent_id} disabled due to repeated errors")

            return {"status": "error", "error": str(e)}

Section 2: Monitoring and Observability

Structured Logging

import json
from datetime import datetime

def log_agent_execution(agent_id, task, result, duration, tokens_used):
    """Log execution in structured format for analysis"""
    log_entry = {
        "timestamp": datetime.utcnow().isoformat(),
        "agent_id": agent_id,
        "task": task,
        "status": result.get("status"),
        "duration_seconds": duration,
        "tokens_used": tokens_used,
        "error": result.get("error"),
    }

    # Send to logging service (CloudWatch, Datadog, etc.)
    logger.info(json.dumps(log_entry))

Metrics to Track

• Success rate: % of tasks completed successfully
• Latency: Time to complete task
• Cost: Tokens used × price per token
• Error rate: Frequency of failures
• Tool usage: Which tools are used most

Section 3: Framework Selection Guide

Section 4: Real-World Example: Trading Agent System

Architecture: Multiple specialized agents managing a trading portfolio:

• Research Agent: Analyzes market data and trends
• Risk Agent: Evaluates portfolio risk and constraints
• Execution Agent: Places trades based on decisions
• Monitor Agent: Tracks performance and alerts on issues

Workflow

async def trading_loop():
    research = await research_agent.analyze_market()
    risk_assessment = await risk_agent.evaluate(research)

    if risk_assessment.approved:
        execution = await execution_agent.place_trade(research)
        await monitor_agent.log_trade(execution)
    else:
        logger.warning("Trade rejected due to risk constraints")

# Run continuously
while True:
    await trading_loop()
    await asyncio.sleep(60)  # Check every minute

Section 5: Lessons Learned

1. Start simple: Single agent with 1-2 tools, then grow
2. Monitor everything: You can't debug what you can't observe
3. Test edge cases: Agents fail in unexpected ways; explicit testing prevents production surprises
4. Human oversight: Critical decisions should involve human review
5. Cost control: Track token usage closely; large-scale agents get expensive fast
6. Iterate constantly: Agent behavior improves with feedback loops and refinement
7. Choose tools wisely: The best framework is the one your team understands and can maintain
8. Plan for failure: Agents will make mistakes; design graceful degradation
9. Document decisions: Future you (and your team) will need to understand why you made certain choices
10. Celebrate wins: When an agent system works, it's genuinely impressive

Capstone Project Ideas

• Customer support system: Multi-agent system routing queries and resolving issues
• Research assistant: Agents that gather, synthesize, and report on topics
• Data pipeline: Agents validating, transforming, and loading data
• Portfolio manager: Agents analyzing and rebalancing investments
• Content creator: Agents researching, writing, editing, and publishing

Key Takeaway

You've gone from understanding LLM fundamentals to building sophisticated multi-agent systems. The future of AI isn't just better models—it's smarter systems that can reason, act, and improve through iteration. You're now equipped to build them.