advanced-evaluation

# 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 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 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`. - Designing autonomous control surfaces, novelty gates, rollback, or PR approval boundaries: `harness-engineering`. - 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, then weight them for the use case. Use model-judged evaluation only after deterministic checks and rubrics are stable. When the work centers on judge prompts, pairwise comparison, calibration, or bias mitigation, switch to Advanced Evaluation. Run deterministic validation before LLM judgment whenever the artifact has machine-checkable structure. Schema validity, duplicate keys, rubric math, manifest sync, retrieval status, and required evidence paths should fail fast before an evaluator spends tokens or returns a subjective score. **Performance Drivers** Apply browsing-agent research when designing evaluation budgets: token usage, tool calls, and model choice can dominate measured performance variance (claim-evaluation-browsecomp-variance). | Factor | Variance Explained | Implication | |--------|-------------------|-------------| | Token usage | Primary driver | More exploration can improve performance until cost or context quality collapses | | Number of tool calls | Secondary driver | More tool use helps only when calls retrieve useful evidence | | Model choice | Secondary but multiplicative | Better models often use tokens and tools more efficiently | Act on these implications when designing evaluations: - **Set realistic token budgets**: Evaluate agents with production-realistic token limits, not unlimited resources. - **Comp