evaluation

To evaluate agent performance, you can use a framework that applies deterministic checks for schema and duplicates, alongside multi-dimensional rubrics to score factual accuracy, completeness, and tool efficiency.

Validating context engineering choices requires running degradation tests and applying deterministic checks to measure how well your context strategies maintain factual accuracy and completeness across varying inputs.

Yes, the framework supports LLM-as-judge methodologies by providing specific guidelines for evaluation, allowing you to combine deterministic rubric math validation with LLM-based scoring of agent responses.

Yes, building this evaluation framework requires Python and the ability to parse rubrics and ground truth, enabling you to perform deterministic checks and score multi-dimensional agent outputs.

Designing test sets for continuous evaluation involves selecting representative samples and stratifying them by complexity, ensuring your agent pipeline is monitored for production quality and schema accuracy.

Agent evaluation may fail if deterministic checks detect schema validation errors, duplicates, or incorrect rubric math before the LLM judgment phase, preventing accurate scoring of factual completeness.