FROM TRACES TO EXPERIMENTS: A LOOP FOR IMPROVING AI AGENTS

Summary from the TLDR DevOps newsletter

Agent optimization should use a continuous loop of trace analysis, hypothesis formation, offline evaluation, controlled production experiments, and post rollout monitoring. Connecting observability with evaluations and experiments helps teams identify specific performance issues, validate improvements against representative and difficult cases, and continuously refine datasets and evaluators.