evaluation

To evaluate AI agent performance systematically, you can design and execute targeted test cases using multi-dimensional rubrics to assess factual accuracy, completeness, and tool efficiency before deployment.

LLM-as-judge is an automated evaluation mechanism where a language model scores agent outputs against multi-dimensional rubrics, providing scalable assessment of factual accuracy and completeness without human intervention.

You can measure citation accuracy and source quality by applying multi-dimensional rubrics that systematically test agent outputs against predefined quality metrics during the evaluation process.

To monitor production AI agents and identify regressions, implement continuous testing frameworks that run agent outputs against established multi-dimensional rubrics and performance metrics over time.

Agent testing supports human evaluation alongside automated metrics by integrating LLM-as-judge methodologies with human oversight to assess factual accuracy, completeness, and tool efficiency.

You need complexity stratification for agent test sets when evaluating performance across varied query difficulties, ensuring the rubrics accurately measure factual accuracy and tool efficiency across all levels.