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Observability
Logging, traces, metrics, and alerts that keep agent incidents from becoming mysteries.
- Observability for AI Agents: monitoring agent systemsβ β βObservability helps track agent behavior through tracing, logs, and metrics.
- Agent tracing: how to track agent decisionsβ β βAgent tracing records each agent step as trace and spans, shows reasoning and tool calls, and helps debug problematic runs in production.
- Distributed tracing for agents: tracing multi-agent systemsβ β βDistributed tracing tracks one run across multiple services, queues, tools, and LLM providers while preserving trace context end-to-end.
- Agent loggingβ β βAgent logging that helps during incidents: trace IDs, tool-call events, stop reasons, redaction strategy, and actionable log structure.
- Semantic logging for agentsβ β βSemantic logging for agents: consistent event taxonomy, structured fields, and queryable traces for debugging and governance audits.
- Agent Metricsβ β βAgent metrics that matter in production: success rate, tool-call volume, retries, cost per run, and drift signals for early detection.
- Tool Usage Metricsβ β βTool usage metrics for agents: call frequency, error rates, retries, and hotspots to optimize reliability, cost, and policy enforcement.
- Agent Cost Monitoringβ β βCost monitoring for AI agents: track model and tool spend per run, set budgets, and alert early before budget explosions.
- Agent Latency Monitoringβ β βLatency monitoring for AI agents: measure p50/p95/p99 across model, tools, and orchestration steps to control user-facing delays.
- AI Agent Health Checks: Liveness, Readiness and Synthetic Runsβ β βSeparate AI agent liveness from readiness, reject stale health signals, and diagnose failures with a runnable Python readiness example.
- Agent Failure Alertingβ β βFailure alerting for agent systems: route stop reasons, dependency outages, and risk signals to on-call teams before incidents escalate.
- Debugging AI Agent Runs: Timeouts, Retries and Reproducible Evidenceβ β βDebug failed AI agent runs with ordered events, repeated tool calls, safe replay, and a runnable Python fixture without external APIs.