Why use outcome-focused evaluation for AI agents?

Since agents are non-deterministic and can reach the same goal through multiple valid paths, outcome-focused evaluation judges the final result rather than forcing a specific execution sequence.

What is the 'LLM-as-judge' methodology?

LLM-as-judge uses a highly capable language model to grade the outputs of an agent based on a structured rubric, providing scalable and consistent qualitative assessments.

What factors most influence agent performance?

Research suggests that token usage explains roughly 80% of performance variance, followed by the number of tool calls and model selection.

How do you test for agent regressions?

By building automated evaluation pipelines that run test sets across different complexity levels, allowing you to compare version performance and catch quality drops before deployment.

AI Agent Evaluation Framework

Name: AI Agent Evaluation Framework
Author: EricGrill

byEricGrill

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Security & Testing

Builds robust evaluation frameworks to measure performance, validate context engineering, and track improvements in agentic systems.

About

The Evaluation skill provides a comprehensive methodology for assessing non-deterministic agent systems, moving beyond traditional software testing to outcome-focused assessment. It enables developers to implement multi-dimensional rubrics covering factual accuracy, tool efficiency, and citation quality while leveraging LLM-as-judge patterns. By incorporating complexity stratification and token-budget analysis, this skill ensures that agentic workflows remain reliable, efficient, and high-performing as context and complexity scale.

Key Features

Automated evaluation pipelines for continuous regression monitoring
LLM-as-judge implementation for scalable, automated assessment
Multi-dimensional rubric design for accuracy, efficiency, and completeness
Complexity stratification for tiered test set development
Context engineering validation and performance degradation testing
2 GitHub stars

Use Cases

Measuring the impact of context engineering on agent decision-making accuracy
Building automated quality gates for production-ready agent deployment
Validating agent performance improvements after model upgrades or prompt changes

About

Key Features

Automated evaluation pipelines for continuous regression monitoring
LLM-as-judge implementation for scalable, automated assessment
Multi-dimensional rubric design for accuracy, efficiency, and completeness
Complexity stratification for tiered test set development
Context engineering validation and performance degradation testing
2 GitHub stars

Use Cases

Measuring the impact of context engineering on agent decision-making accuracy
Building automated quality gates for production-ready agent deployment
Validating agent performance improvements after model upgrades or prompt changes