evaluation

You can evaluate agent performance by running a systematic test suite against the new version to compare metrics like factual accuracy, completeness, and tool efficiency, ensuring no regression occurred from prompt changes.

LLM-as-judge is an evaluation methodology where a language model assesses agent performance using multi-dimensional rubrics to measure factual accuracy, citation accuracy, and source quality systematically.

Yes, you can implement quality gates using multi-dimensional rubrics to ensure agent performance meets defined standards for factual accuracy and tool efficiency before pipeline deployment.

The best way to measure accuracy in non-deterministic AI systems is applying multi-dimensional rubrics through systematic testing frameworks that support both LLM-as-judge and human evaluation methodologies.

You can set up continuous monitoring by integrating systematic test frameworks and quality gates into your agent pipelines, enabling ongoing assessment of factual accuracy, completeness, and tool efficiency.