The Agent Observability Stack: Logs, Traces, and Metrics

AI agents introduce a new class of observability challenge: non-deterministic behavior, variable execution paths, and emergent failures that do not show up in traditional application monitoring. The three-layer observability stack: Logs capture every input, output, tool call, and error for each agent run — structured JSON with consistent fields. Traces show the complete execution path through multi-agent workflows — OpenTelemetry is the standard, with Jaeger or Tempo as the backend. Metrics aggregate signal across all runs — task completion rate, latency percentiles, token usage, cost per run, and error rate by category. The hardest part is correlating across all three layers: a specific trace ID should be present in logs, the trace backend, and aggregated into the right metric dimensions. LangSmith, AgentOps, and Helicone are purpose-built for LLM observability and reduce instrumentation time from weeks to days. For teams already invested in Datadog or Grafana, the OpenTelemetry exporter path is the pragmatic choice.