Offline evaluation for AI agents: Best practices

The article argues that offline evaluation is essential for LLM-powered applications to prevent regressions and avoid the risks of relying on unpredictable user feedback in production. It outlines a framework centered on using annotated data, application tasks, and scoring evaluators to help developers reliably benchmark changes and iterate on AI agents with greater confidence.