Topic 12: Production Monitoring
What You’ll Learn
This topic teaches you how to:
- Monitor agents in real-time
- Track performance metrics
- Collect error data
- Gather user feedback
- Continuously improve agents
Why We Need This
Business Need
- Reliability: Ensure agents work in production
- Performance: Track and optimize performance
- User satisfaction: Monitor user feedback
Technical Need
- Monitoring: Real-time agent monitoring
- Metrics: Track key metrics
- Alerting: Alert on issues
Industry Use Cases
1. Production Monitoring
Company: All production systems Use Case: Monitor agents 24/7
2. Performance Tracking
Company: Agent platforms Use Case: Track performance over time
3. User Feedback
Company: Customer-facing agents Use Case: Collect and analyze user feedback
Industry-Standard Boilerplate Code
Production Monitor
"""
Production Monitor
Monitors agents in production
"""
from typing import Dict, List
from datetime import datetime
class ProductionMonitor:
"""Monitor agents in production"""
def __init__(self):
self.metrics: List[Dict] = []
def track_execution(self, agent_name: str, task: str, result: Dict):
"""Track agent execution"""
self.metrics.append({
"timestamp": datetime.now().isoformat(),
"agent": agent_name,
"task": task,
"success": result.get('success', False),
"tokens": result.get('tokens', 0),
"time": result.get('time', 0)
})
def get_metrics(self) -> Dict:
"""Get aggregated metrics"""
if not self.metrics:
return {}
return {
"total_executions": len(self.metrics),
"success_rate": sum(1 for m in self.metrics if m['success']) / len(self.metrics),
"avg_tokens": sum(m['tokens'] for m in self.metrics) / len(self.metrics),
"avg_time": sum(m['time'] for m in self.metrics) / len(self.metrics)
}
Exercises
- Set up monitoring
- Track metrics
- Collect feedback
- Create alerts
Next Steps
- Review all topics
- Build complete evaluation system
- Deploy to production