evaluationlisted

# Evaluation Methods for Agent Systems Evaluate agent systems differently from traditional software because agents make dynamic decisions, are non-deterministic between runs, and often lack single correct answers. Build evaluation frameworks that account for these characteristics, provide actionable feedback, catch regressions, and validate that context engineering choices achieve intended effects. ## When to Activate - 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 Do not activate this skill for adjacent work owned by other skills: - Designing the LLM judge itself, pairwise comparison, judge calibration, or bias mitigation: `advanced-evaluation`. - Debugging a specific context failure mode before measuring it: `context-degradation`. ## Core Concepts Focus evaluation on outcomes rather than execution paths, because agents may find alternative valid routes to goals. Judge whether the agent achieves the right outcome via a reasonable process, not whether it followed a specific sequence of steps. Use multi-dimensional rubrics instead of single scores because one number hides critical failures in specific dimensions. Capture factual accuracy, completeness, citation accuracy, source quality, and tool efficiency as separate dimensions