LangSmith closes the loop between traces, evals, and production feedback

Agent products need more than raw telemetry: teams need a feedback loop that turns live failures into reproducible evaluation cases. LangSmith's observability documentation describes full visibility from individual traces to production-wide performance metrics, with tracing setup, trace filtering, dashboards, alerts, automations, and user feedback. Its evaluation documentation separates offline evaluation for pre-ship datasets and experiments from online evaluation for live production interactions, including human review, code rules, LLM-as-judge, pairwise comparison, sampling controls, anomaly detection, and alerting. The strongest signal is the loop between the two: failing production traces can be added to datasets, targeted evaluators can be created, fixes can be validated with offline experiments, and the improved app can be redeployed. For agent builders, observability platforms should be judged by whether traces, feedback, datasets, online evals, experiments, and regression tests are connected enough to improve agent behavior over time instead of merely recording what went wrong.