Agent Observability

Agent observability is the ability to see inside a run. Because agents make their own decisions, when one goes wrong the only way to understand why is to trace every step: what the model saw, what it decided, which tools it called, what they returned, and where it went off track. Without that trace, debugging an agent is guesswork. A good trace shows the full timeline of a run with inputs, outputs, token counts, latency, and errors at each step. That lets you find the prompt that confused the model, the tool that returned garbage, or the loop that never terminated. It is also how you measure cost, since token spend adds up fast across multi-step, multi-agent runs. Several frameworks ship observability: LangGraph integrates LangSmith, Pydantic AI uses Logfire, AutoGen and Semantic Kernel expose OpenTelemetry, and CrewAI and Mastra include dashboards. If a framework has none built in, you can usually add OpenTelemetry or a third-party tracer. Treat observability as a requirement, not a nice-to-have, before any agent goes to production.