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Deployment Strategies

Monitoring & Observability

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

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Why Monitoring Matters

Production AI agents are invisible until they break. Without observability, you're flying blind: agents fail silently, performance degrades unnoticed, costs spiral out of control. Monitoring transforms mystery into visibility. Log every decision. Trace every request. Measure everything that matters. When agents break at 3am, good observability means 5-minute diagnosis, not 5-hour detective work.

Interactive: Log Level Explorer

Understanding when to use each log level is critical. Click each level to see appropriate use cases:

The Three Pillars of Observability

📝 Logs (What Happened)

Discrete events with timestamps. "User requested X", "API returned Y", "Error occurred".

📊 Metrics (How Much)

Numerical data over time. Request count, latency percentiles, error rates, costs.

🔗 Traces (Why Slow)

Request journey across services. See where time is spent, identify bottlenecks.

💡
Without Observability vs With It
❌ Without:
  • â€ĸ "Agent stopped responding" (no logs)
  • â€ĸ Cost spike from $100→$10k (no alerts)
  • â€ĸ 5-hour debugging sessions (no traces)
  • â€ĸ Silent failures affecting 20% of users
✅ With:
  • â€ĸ Logs show exact failure point instantly
  • â€ĸ Alert fired when cost exceeded $200
  • â€ĸ Trace reveals slow database query
  • â€ĸ Dashboard shows 20% error rate spike