evaluation

This skill provides a rigorous framework for evaluating non-deterministic AI agent systems. It shifts the focus from simple unit testing to outcome-based validation, addressing the challenges of agentic behavior where execution paths vary but desired goals remain constant. The skill is designed for engineers, researchers, and AI architects tasked with building, testing, and iterating on production-grade agent pipelines. It emphasizes catching regressions early, optimizing context usage, and establishing quality gates that ensure reliability across complex interaction patterns.

• Multi-dimensional rubric design: Score agents across discrete dimensions such as factual accuracy, completeness, citation integrity, source quality, and tool efficiency to identify specific failure modes.

• LLM-as-a-judge implementation: Deploy scalable, model-based evaluation prompts to assess large test sets while mitigating bias by utilizing diverse model families for evaluation.

• Performance driver analysis: Apply data-backed insights like the '95% Finding' to optimize token budgets, model selection, and tool usage to maximize agentic performance.

• Regression testing and quality gates: Integrate systemic testing into CI/CD workflows to prevent performance degradation as agent configurations or system prompts evolve.

• Hybrid evaluation strategy: Combine automated LLM-based scoring with targeted human review for edge cases, hallucination detection, and bias mitigation.

• Target metrics: Focus on outcomes rather than hard-coded execution paths, as agents are inherently non-deterministic.

• Input requirements: Expects test sets including ground truth, varied complexity queries, and production-representative interaction history.

• Constraints: Be mindful of token usage limits; production-realistic evaluation requires balancing cost, speed, and accuracy.

• Practical tips: Always weight dimensions according to your specific use case (e.g., prioritize accuracy for research tasks, efficiency for cost-sensitive automation).

• Integration: This skill is intended for use with frameworks like the Vercel AI SDK, LangSmith, or internal evaluation pipelines where agentic reasoning traces or structured logs are available.