What factors most impact agent performance?

Research indicates that token usage, the number of tool calls allowed, and the specific model choice account for approximately 95% of performance variance in complex agents.

What is 'LLM-as-judge' in this context?

It is a methodology using a high-capability language model to evaluate the outputs of other models or agents based on structured rubrics, allowing for scalable testing of subjective quality.

Can I use this skill for monitoring production agents?

Yes, the skill includes guidance on sampling production interactions and tracking evaluation metrics over time to detect quality regressions and performance trends.

Why is agent evaluation different from standard software testing?

Agents are non-deterministic and can achieve goals through multiple valid paths, requiring outcome-focused rubrics rather than static code assertions.

How does this skill help with context engineering?

It provides methods to run comparative evaluations on different context strategies, helping you find the most token-efficient and accurate approach for your specific use case.

Agent Performance Evaluation

Name: Agent Performance Evaluation
Author: muratcankoylan

bymuratcankoylan

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5,498

보안 및 테스팅

Measures and optimizes AI agent performance through multi-dimensional rubrics, LLM-as-judge methodologies, and robust testing frameworks.

소개

This skill provides a comprehensive framework for evaluating complex agentic systems where traditional software testing falls short. It addresses the challenges of non-determinism and context-dependent failures by implementing outcome-focused evaluation methods, multi-dimensional scoring rubrics, and systematic test set design. Whether you are validating context engineering choices or building quality gates for production pipelines, this skill offers the guidance needed to measure improvements, catch regressions, and ensure agent reliability across various complexity levels from simple lookups to deep research synthesis.

주요 기능

LLM-as-judge implementation for scalable and consistent automated testing
5,498 GitHub stars
Context engineering validation to optimize token budgets and identify performance cliffs
Complexity-stratified test set creation to cover simple to very complex reasoning tasks
Multi-dimensional rubric design for factual accuracy, completeness, and tool efficiency
Continuous evaluation pipeline integration for production monitoring and regression detection

사용 사례

Comparing the effectiveness of different LLM models within a multi-agent architecture
Building automated quality gates for agentic workflow deployments
Optimizing context window management by identifying where agent performance degrades

소개

주요 기능

LLM-as-judge implementation for scalable and consistent automated testing
5,498 GitHub stars
Context engineering validation to optimize token budgets and identify performance cliffs
Complexity-stratified test set creation to cover simple to very complex reasoning tasks
Multi-dimensional rubric design for factual accuracy, completeness, and tool efficiency
Continuous evaluation pipeline integration for production monitoring and regression detection

사용 사례

Comparing the effectiveness of different LLM models within a multi-agent architecture
Building automated quality gates for agentic workflow deployments
Optimizing context window management by identifying where agent performance degrades