evaluation

To build an evaluation framework for non-deterministic agent systems, you need multi-dimensional rubrics that assess factual accuracy, tool efficiency, and context-dependency, supported by LLM-as-judge and human evaluation methodologies.

The LLM-as-judge methodology for agent performance testing uses scalable automated evaluation to assess multi-path solutions and composite quality dimensions, integrating essential human oversight to handle edge cases and validate context engineering.

You design test sets and rubrics for context engineering validation by creating multi-dimensional criteria that measure factual accuracy, completeness, citation accuracy, source quality, and tool efficiency across various query complexities.

Yes, you can use this evaluation framework for continuous evaluation pipelines before deployment to systematically test agent performance across various query complexities, ensuring the system meets accuracy and efficiency standards.

The best way to evaluate composite quality dimensions in AI agents is combining LLM-as-judge for scalable automated evaluation with human evaluation methodologies to address non-determinism and multi-path solution challenges.