agent-evaluation

Evaluating LLM agents in production requires running behavioral regression tests, capability assessments, and reliability metrics to expose gaps between benchmark success and real-world behavior. These multi-metric evaluations detect flaky or unsafe outputs before deployment.

Behavioral regression testing for LLM agents evaluates non-deterministic outputs where the same input produces different results. It ensures reliability metrics are met and catches unsafe outputs, recognizing that the goal isn't a 100% test pass rate but consistent real-world safety.

Running a multi-metric agent evaluation involves applying capability assessments alongside behavioral regression tests and reliability metrics in production-like scenarios. This combination catches issues before production by evaluating how agents perform across diverse real-world tasks.

Agent evaluation works without traditional pass rates because evaluating LLM agents fundamentally differs from testing traditional software. It uses reliability metrics to evaluate non-deterministic outputs where correct often has no single answer, preventing spectacular production failures.

Reliability metrics for evaluating LLM agents are needed when benchmark success fails to translate to production stability. These metrics detect flaky or unsafe outputs, satisfying requirements for behavioral regression testing and capability assessment within evaluation frameworks.