From traces to experiments: A loop for improving AI agents

To effectively optimize AI agentic systems, teams should use aggregate trace data to identify specific performance bottlenecks and formulate testable hypotheses. The article proposes a continuous optimization loop that integrates offline evaluations on curated datasets with production experimentation to ensure that changes improve quality, cost, and latency without causing regressions.