Why do agents need different evaluation methods than standard software?

Agents are non-deterministic and can follow multiple valid paths to a goal; therefore, evaluation must focus on outcomes and multi-dimensional rubrics rather than static code assertions.

What is 'LLM-as-judge' in agent evaluation?

It's a methodology that uses a high-capability language model to evaluate the outputs of other agents based on specific criteria, providing scalable and consistent quality assessments that mimic human judgment.

How does this skill help with context engineering?

It provides methods for testing how different context strategies and token budgets affect agent performance, helping you identify performance cliffs and optimize the information density provided to the model.

What are the most important factors for agent performance?

Research suggests that token usage, the number of tool calls, and model choice explain 95% of performance variance, making these the critical metrics to track during evaluation.

Agent Evaluation & Quality Assurance

Name: Agent Evaluation & Quality Assurance
Author: muratcankoylan

bymuratcankoylan

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7,140

セキュリティとテスト

Builds robust evaluation frameworks and multi-dimensional rubrics to measure, optimize, and validate AI agent performance and context efficiency.

概要

This skill provides specialized guidance for testing and validating AI agent systems, moving beyond traditional software testing to address non-determinism and complex reasoning paths. It enables developers to implement LLM-as-judge methodologies, design outcome-focused rubrics, and validate context engineering strategies through systematic performance measurement. By focusing on multi-dimensional quality gates—including factual accuracy, tool efficiency, and token usage—it helps teams catch regressions, optimize token budgets, and ensure agents reliably achieve their goals in production environments.

主な機能

LLM-as-judge implementation patterns for scalable automated testing
Context engineering validation and performance degradation testing
Complexity-stratified test set construction for production-grade benchmarking
Multi-dimensional rubric design covering accuracy, completeness, and tool efficiency
Outcome-focused evaluation frameworks for non-deterministic agent paths
7,140 GitHub stars

ユースケース

Building automated quality gates for multi-agent production pipelines
Detecting performance regressions and identifying token budget optimizations
Comparing agent performance across different LLM models and context strategies

概要

主な機能

LLM-as-judge implementation patterns for scalable automated testing
Context engineering validation and performance degradation testing
Complexity-stratified test set construction for production-grade benchmarking
Multi-dimensional rubric design covering accuracy, completeness, and tool efficiency
Outcome-focused evaluation frameworks for non-deterministic agent paths
7,140 GitHub stars

ユースケース

Building automated quality gates for multi-agent production pipelines
Detecting performance regressions and identifying token budget optimizations
Comparing agent performance across different LLM models and context strategies