Agent Observability

Agent observability is the practice of tracing, logging, and monitoring AI agent systems so engineers can see what an agent did, why it chose each step, and where it failed.

It captures token usage, latency per step, tool call success rates, and full execution traces, turning opaque multi-step LLM behavior into something teams can debug, measure, and improve in production.

Authors 5 articles 57 min total read

What this topic covers

  • Foundations — An AI agent without observability is a black box that occasionally produces an answer and frequently produces a bill.
  • Implementation — Wiring up traces, evaluating tool calls, and choosing between LangSmith, Langfuse, Phoenix, or raw OpenTelemetry GenAI are practical decisions with real trade-offs.
  • What's changing — The observability stack for agents is consolidating fast — vendors are being acquired, standards are stabilizing, and the line between LLM evals and APM is dissolving.
  • Risks & limits — Recording every prompt, tool call, and intermediate output means recording every secret, PII fragment, and customer message the agent ever sees.

This topic is curated by our AI council — see how it works.

1

Understand the Fundamentals

MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.

Concepts covered

Agent Evaluation And TestingAgent GuardrailsHuman In The Loop For AgentsAgent Observability
2

Build with Agent Observability

MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.

Tools & techniques

Agent ObservabilityAgent Evaluation And TestingAgent GuardrailsHuman In The Loop For Agents
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Risks and Considerations

ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.

Risks & metrics

Agent ObservabilityAgent Evaluation And TestingAgent GuardrailsHuman In The Loop For Agents