Monitoring & Observability
Master monitoring and observability for production AI agents including logging, tracing, metrics, and real-time debugging
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Production AI agents are invisible until they break. Observability makes the invisible visible. Log every decision with structured JSON. Trace every request with correlation IDs. Measure everything that matters: latency, errors, cost. Dashboard health at a glance. Alert only on user impact. Debug fast: dashboard → trace → logs. Good observability means 5-minute diagnosis instead of 5-hour detective work. Invest in monitoring; it pays for itself during the first incident.
Your Observability Stack
Logs: Structured JSON (timestamp, trace_id, user_id, action, duration, result). Tools: Datadog, Splunk, CloudWatch.
Traces: Distributed tracing with correlation IDs. Tools: Jaeger, Zipkin, AWS X-Ray, Datadog APM.
Metrics: Time-series data (requests/sec, error %, latency percentiles, cost). Tools: Prometheus, Grafana, Datadog, CloudWatch.
Dashboards: Single-pane-of-glass views with red/yellow/green health indicators. Tools: Grafana, Datadog, Kibana.
Alerts: Severity-based (INFO/WARN/ERROR). Integration: PagerDuty, Slack, OpsGenie.