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Monitoring & Observability

Master monitoring and observability for production AI agents including logging, tracing, metrics, and real-time debugging

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Logging & Tracing

Logs tell you WHAT happened. Traces tell you WHY it was slow. Every agent action should log: timestamp, request ID, user ID, tool called, duration, result. Structure logs as JSON for easy parsing. Use trace IDs to follow requests across services. When debugging, grep logs by trace ID to see entire request journey. Distributed tracing reveals the 320ms database query hiding in your 2-second response time.

Interactive: Request Trace Visualizer

Watch how a single user request flows through multiple services. Click "Start Trace" to follow the journey:

Request Trace
Trace ID: trace_xyz789
API Gateway
Receive Request
5ms
Agent Orchestrator
Parse Intent
45ms
Knowledge Base
Query Documents
320ms
LLM Service
Generate Response
1850ms
Slow!
API Gateway
Return Response
8ms

Structured Logging Best Practices

❌ Bad Logging:
"User did something"
Missing: who, what, when, context. Can't search or analyze.
✅ Good Logging:
{"timestamp": "2025-11-18T10:30:45Z", "trace_id": "xyz789", "user_id": "user_123", "action": "search_documents", "duration_ms": 320}
Structured, searchable, includes all context.
💡
Correlation IDs Are Critical

Generate a unique trace_id for every request. Pass it through ALL services. When debugging, search logs by trace_id to see the complete story: API gateway → orchestrator → knowledge base → LLM → response. Without correlation IDs, you're blind to how services interact. With them, debugging is 10x faster.

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