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    Definitive answer

    How do you monitor AI agents in production?

    Monitor AI agents in production by emitting OpenTelemetry-compatible traces for every step, defining SLOs for quality, cost, and latency, running continuous evals on sampled traffic, alerting on drift and anomaly, routing low-confidence outputs to human review, and feeding production failures back into the golden test set on a weekly cadence.

    What to instrument

    Every agent step is a span. Capture inputs, outputs, tool calls, retrievals, latency, tokens, cost, and human edits.

    • Per-step spans with prompt + completion + tool args.
    • Retrieval scores and chunk IDs for grounding audits.
    • Cost per task, p50/p95 latency, success rate.
    • Human override events as ground-truth labels.

    Tooling and runbooks

    Implement Agentic standardizes on Langfuse, LangSmith, Braintrust, or Arize Phoenix depending on compliance posture, paired with PagerDuty or Opsgenie for on-call. Runbooks cover prompt rollback, model fallback, tool circuit breaking, and evaluation regression triage.

    Frequently asked

    What SLOs do agents need?+

    At minimum: task success rate, p95 latency, cost per task, and a quality score from continuous evals. SLO violation triggers paging or auto-rollback.

    How do you detect drift?+

    Compare rolling-window eval scores against the baseline. Statistically significant degradation triggers an alert and a re-eval against the full golden set.

    Related reading

    AI Governance for Agentic Systems (canonical guide)Enterprise LLM Evaluation Framework (canonical guide)AI Governance & LLM EvaluationHow enterprises evaluate LLM agentsWhat are the failure modes of enterprise AI agents?

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