evaluation

1# Evaluation Methods for Agent Systems
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3Evaluate 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.
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5## When to Activate
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7Activate this skill when:
8- Testing agent performance systematically
9- Validating context engineering choices
10- Measuring improvements over time
11- Catching regressions before deployment
12- Building quality gates for agent pipelines
13- Comparing different agent configurations
14- Evaluating production systems continuously
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16Do not activate this skill for adjacent work owned by other skills:
17- Designing the LLM judge itself, pairwise comparison, judge calibration, or bias mitigation: `advanced-evaluation`.
18- Designing autonomous control surfaces, novelty gates, rollback, or PR approval boundaries: `harness-engineering`.
19- Debugging a specific context failure mode before measuring it: `context-degradation`.
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21## Core Concepts
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23Focus 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.
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