evaluation

Evaluating AI agent performance requires methodologies that address non-determinism by using rubrics, LLM-as-Judge, and end-state evaluation. This allows you to systematically test agent performance and validate context engineering choices despite varying outputs.

Building quality gates for LLM agent pipelines involves applying systematic evaluation rubrics and complexity stratification to test results. You can compare different agent configurations against predefined quality standards before deploying to production.

LLM-as-Judge works by using a language model to evaluate agent responses against specific rubrics and quality standards. It provides a scalable methodology to measure agent performance and validate context engineering without relying solely on human evaluation.

Yes, degradation testing measures context dependency by stratifying complexity and evaluating the end-state of agent systems. This approach identifies how agent performance degrades under varying conditions and validates context engineering choices.

Human evaluation is necessary when assessing nuanced context-dependency and subjective quality standards that automated rubrics or LLM-as-Judge cannot capture. It complements systematic testing by validating agent performance against human expectations.

End-state evaluation limitations include overlooking intermediate reasoning errors in non-deterministic agents and missing context-dependency issues. Degradation testing and complexity stratification are required to capture the full scope of agent performance variations.