agent-evaluation

Multi-dimensional rubric evaluation assesses agent outputs across multiple criteria—factual accuracy, completeness, citation quality, and tool efficiency—rather than a single metric. This Skill applies rubrics to non-deterministic agent behavior, providing scalable, objective assessments that capture quality across multiple dimensions simultaneously.

Yes. LLM-as-judge integration scales evaluation across large test sets by leveraging language models to score outputs against your rubrics. This Skill includes LLM-as-judge support to automate scoring, reducing manual review overhead while maintaining consistency across evaluations.

Continuous evaluation involves designing test sets, selecting evaluation methodologies, and storing evaluation history to track quality trends over time. This Skill supports test-set design and continuous evaluation scenarios, letting you monitor agent performance methodically and identify improvement areas.

Multi-path agent systems need evaluation frameworks that account for non-deterministic behavior and varying routes to the goal. This Skill develops evaluation frameworks specifically for agents achieving objectives through multiple paths, scoring each against your rubrics to ensure quality across all outcomes.

Rubric-based evaluation requires well-defined scoring criteria and clear test data; incomplete rubrics may miss important quality dimensions. This Skill addresses pitfalls in rubric design and evaluation methodology, helping you anticipate common challenges in agent assessment.

You need representative agent outputs and a clear understanding of quality dimensions relevant to your use case. This Skill guides you through framework development by supporting test-set design alongside rubric creation, so you can build evaluation pipelines tailored to your agent's goals.