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.
Questa competenza deve essere utilizzata quando l'utente chiede di "valutare le prestazioni dell'agente", "creare un framework di test", "misurare la qualità dell'agente", "creare rubriche di valutazione" o menziona LLM come giudice, valutazione multidimensionale, test degli agenti o controlli di qualità per le pipeline degli agenti. Fonte: guanyang/antigravity-skills.