evaluation

Evaluate agent performance by defining multi-dimensional rubrics that measure accuracy, completeness, and efficiency. This framework uses methodologies like LLM-as-judge and human evaluation to systematically score non-deterministic agent behavior.

LLM-as-judge is an evaluation methodology where a language model scores agent outputs against predefined rubrics. It automates quality assurance by grading non-deterministic responses across multiple dimensions like accuracy and completeness.

Design a test set by creating cases that validate context engineering choices and define quality gates. Running the agent against this suite before deployment ensures performance meets predefined rubrics and catches regressions.

Yes, this evaluation framework supports context engineering evaluation by validating your context choices against a designed test suite. It systematically measures how well the agent handles context-dependent scenarios.

Continuously monitor agent performance by applying this framework's quality gates and rubrics to ongoing interactions. This identifies performance drivers and addresses context-dependent failures in non-deterministic agent behavior over time.

Non-determinism makes agent evaluation challenging because identical inputs can produce different outputs without a single correct answer. This framework overcomes the challenge by applying multi-dimensional rubrics and LLM-as-judge methodologies.