agent-evaluation

Evaluate and optimize LLM agent output quality using MLflow APIs.

Updated Mar 10, 2026

Search Tags:#quality assurance #optimization #llm #agent #evaluation #mlflow

Ready to run

npx skills add https://github.com/slysik/databricks-claude-coding --skill agent-evaluation-slysik

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/slysik/databricks-claude-coding/tree/main/.agents/skills/agent-evaluation
Command: npx skills add https://github.com/slysik/databricks-claude-coding --skill agent-evaluation-slysik

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of systematically evaluating and improving the output quality of Large Language Model (LLM) agents, ensuring they are accurate, cost-effective, and reliable.

Core Features & Use Cases

End-to-End Evaluation: Guides users through the complete process of evaluating LLM agents using MLflow.
Component-Specific Evaluation: Allows for focused evaluation of individual components like tracing setup, dataset creation, or scorer definition.
MLflow Integration: Mandates the use of MLflow APIs for all evaluation tasks to ensure observability and reproducibility.
Use Case: Improve an agent's tool selection accuracy by evaluating its performance on a curated dataset with specific quality metrics.

Quick Start

Use the agent-evaluation skill to evaluate your agent's output quality using MLflow.

Frequently Asked Questions about agent-evaluation

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

FAQPage Schema

How do I evaluate LLM agent output quality using MLflow?▼

To evaluate LLM agent output quality, this skill guides you through end-to-end MLflow evaluation workflows, including tracing setup, dataset creation, scorer definition, and execution using native MLflow APIs.

What is the best way to optimize LLM agent tool selection and cost-efficiency?▼

Optimizing LLM agent tool selection and cost-efficiency involves evaluating agent performance on a curated dataset using specific quality metrics and scorers defined through MLflow's native APIs.

Can I evaluate individual components of an LLM agent instead of running full end-to-end evaluation?▼

Yes, you can evaluate individual components like tracing setup, dataset creation, or scorer definition independently, allowing for focused evaluation without running the complete end-to-end MLflow workflow.

Do I need a specific MLflow version to evaluate LLM agents?▼

Yes, evaluating LLM agents requires MLflow 3.8 or higher. You must also adhere to MLflow APIs for datasets, scorers, and evaluation to ensure proper observability and reproducibility.

Why should I use MLflow APIs for LLM agent evaluation instead of custom scripts?▼

Using MLflow APIs for LLM agent evaluation ensures observability and reproducibility. This skill mandates MLflow integration to systematically evaluate and improve output accuracy, cost-effectiveness, and reliability.

How does systematic LLM agent evaluation improve agent reliability?▼

Systematic LLM agent evaluation improves reliability by measuring output quality, tool selection, and cost-efficiency against curated datasets, ensuring agents are accurate and cost-effective.