agent-evaluation

Automate evaluation and benchmarking of AI agents and LLM-powered systems.

Updated Mar 17, 2026

Search Tags:#latency #regression-testing #llm-testing #behavioral-testing #agent-evaluation #benchmark-design #reliability-metrics

Ready to run

npx skills add https://github.com/HemantSudarshan/Dhumichatbot --skill agent-evaluation-hemantsudarshan

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/HemantSudarshan/Dhumichatbot/tree/main/skills/01-ai-core/agent-evaluation
Command: npx skills add https://github.com/HemantSudarshan/Dhumichatbot --skill agent-evaluation-hemantsudarshan

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Evaluates AI agents with rigorous benchmarks to catch behavioral gaps, reliability issues, and performance regressions that simple tests miss.

Core Features & Use Cases

Capabilities include: agent-testing, benchmark-design, capability-assessment, reliability-metrics, regression-testing.
Requirements cover testing-fundamentals and llm-fundamentals to ensure robust evaluation.
Patterns such as Statistical Test Evaluation, Behavioral Contract Testing, and Adversarial Testing to ensure resilience.
Anti-Patterns to avoid single-run-testing, only-happy-path-tests, and output-string-matching.

Quick Start

Outline a default evaluation plan by specifying metrics, datasets, and test scenarios for an AI agent.

Frequently Asked Questions about agent-evaluation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I benchmark AI agents to catch reliability issues and behavioral regressions?▼

Design agent benchmarks using Statistical Test Evaluation, Behavioral Contract Testing, and Adversarial Testing patterns. These frameworks validate agent behavior and reliability across capabilities, preventing single-run or happy-path testing gaps.

What is behavioral contract testing for LLM-powered systems?▼

Behavioral contract testing is an evaluation pattern for LLM-powered systems that validates agent behavior against defined expectations. It ensures resilience by testing reliability metrics across scenarios rather than relying on output string matching.

How do I set up a default evaluation plan for an AI agent?▼

Set up an AI agent evaluation plan by explicitly specifying metrics, datasets, and test scenarios. This outlines a comprehensive benchmarking strategy to assess agent capabilities and collect performance results.

Do I need testing fundamentals to evaluate LLM agent performance?▼

Yes, evaluating LLM agent performance requires testing fundamentals and LLM fundamentals. These prerequisites ensure robust evaluation by supporting rigorous metric definitions and test dataset management.

Why does single-run testing fail to catch AI agent regressions?▼

Single-run testing fails to catch AI agent regressions because it is an anti-pattern that evaluates only happy-path outputs. Comprehensive benchmarking requires statistical test evaluation across multiple datasets to ensure reliability.

What are the limitations of output string matching for agent evaluation?▼

Output string matching is an anti-pattern limitation in agent evaluation because it misses behavioral nuances. Validating AI agents requires statistical test evaluation and adversarial testing to accurately measure reliability metrics.