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.

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