evaluation

SKILL.md

Evaluation of agent systems requires different approaches than traditional software or even standard language model applications. Agents make dynamic decisions, are non-deterministic between runs, and often lack single correct answers. Effective evaluation must account for these characteristics while providing actionable feedback. A robust evaluation framework enables continuous improvement, catches regressions, a...

Agent evaluation requires outcome-focused approaches that account for non-determinism and multiple valid paths. Multi-dimensional rubrics capture various quality aspects: factual accuracy, completeness, citation accuracy, source quality, and tool efficiency. LLM-as-judge provides scalable evaluation while human evaluation catches edge cases.

The key insight is that agents may find alternative paths to goals—the evaluation should judge whether they achieve right outcomes while following reasonable processes.

이 기술은 사용자가 "에이전트 성능 평가", "테스트 프레임워크 구축", "에이전트 품질 측정", "평가 기준표 생성"을 요청하거나 LLM-판사, 다차원 평가, 에이전트 테스트 또는 에이전트 파이프라인에 대한 품질 게이트를 언급할 때 사용해야 합니다. 출처: muratcankoylan/agent-skills-for-context-engineering.

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