Agent Performance Evaluation

About

The Agent Performance Evaluation skill provides a comprehensive framework for measuring the quality and reliability of autonomous agent systems. It addresses the unique challenges of AI testing—such as non-determinism and context-dependent failures—by providing standardized rubrics for factual accuracy, tool efficiency, and citation quality. Whether you are building quality gates for production pipelines or validating context engineering strategies, this skill helps you implement scalable LLM-as-judge patterns and complexity-stratified test sets to ensure your agents perform consistently across diverse scenarios.

Key Features

• Complexity-stratified test set design (Simple to Very Complex)
• Performance variance analysis based on token usage and tool efficiency
• LLM-as-judge scoring patterns for automated, scalable quality assessment
• Continuous evaluation pipelines for detecting regressions in agent behavior
• 114 GitHub stars
• Multi-dimensional evaluation rubrics for factual and process accuracy

Use Cases

• Building automated quality gates for agent-based deployment pipelines
• Comparing the effectiveness of different context engineering strategies
• Measuring performance gains when upgrading models or expanding toolsets