evaluation

Evaluate AI agent performance using multi-dimensional rubrics by defining criteria for factual accuracy, completeness, citation accuracy, source quality, and tool efficiency, then applying these rubrics systematically across test sets to measure quality.

LLM-as-judge is an automated evaluation methodology where a language model scores agent responses against defined rubrics, enabling scalable performance metrics without manual human evaluation for continuous quality assurance.

Create test sets stratified by complexity for agent testing by using test set management tools to generate, filter, and analyze queries, ensuring evaluation covers diverse difficulty levels and context-dependent failure modes.

Monitor live agent performance and catch regressions in production by using production monitoring features to sample and track agent responses against baseline metrics, addressing non-determinism and context-dependent failures continuously.

Manual human evaluation works alongside automated LLM-as-judge methodologies by providing complementary validation, allowing teams to cross-reference automated performance metrics with manual rubric scoring for comprehensive quality assurance.

Agent evaluation often fails on non-deterministic outputs because identical queries produce varying responses, requiring complexity stratification in test sets and multi-dimensional rubrics to validate context-dependent failures rather than exact matches.