evaluation

## When to Use This Skill Build evaluation frameworks for agent systems Use this skill when working with build evaluation frameworks for agent systems. # Evaluation Methods for Agent Systems 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, and validates that context engineering choices achieve intended effects. ## When to Use Activate this skill when: - Testing agent performance systematically - Validating context engineering choices - Measuring improvements over time - Catching regressions before deployment - Building quality gates for agent pipelines - Comparing different agent configurations - Evaluating production systems continuously ## Core Concepts 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 ri...