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agent-evaluation

Evaluate LLM agent output quality using MLflow APIs for datasets and scorers.

3|1|Updated May 12, 2025
Search Tags:#quality assurance#evaluation#tool selection#mlflow#llm agent#agent improvement
One-click install
npx skills add https://github.com/Aradhya0510/databricks-cv-accelerator --skill agent-evaluation-aradhya0510
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/Aradhya0510/databricks-cv-accelerator/tree/main/.github/skills/agent-evaluation
Command: npx skills add https://github.com/Aradhya0510/databricks-cv-accelerator --skill agent-evaluation-aradhya0510

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a systematic framework for evaluating and improving the output quality of Large Language Model (LLM) agents, addressing issues like incorrect tool selection, poor answer quality, high costs, and inaccurate responses.

Core Features & Use Cases

  • End-to-End Evaluation: Covers the complete workflow from tracing setup to evaluation execution.
  • MLflow Integration: Leverages MLflow's native APIs for datasets, scorers, and evaluation for robust tracking and observability.
  • Systematic Improvement: Helps optimize tool selection accuracy, reduce costs, and fix agent errors.
  • Use Case: You have an LLM agent that is supposed to book flights but sometimes suggests incorrect dates or uses the wrong API. This skill helps you evaluate its performance against a set of test cases, identify the root cause of errors, and implement improvements.

Quick Start

Use the agent-evaluation skill to evaluate the 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?▼

You evaluate LLM agent output quality by leveraging MLflow's native APIs for datasets, scorers, and tracing to systematically test, track, and improve agent responses. This framework specifically addresses tool selection accuracy, answer correctness, and cost reduction.

What is the best way to fix incorrect tool selection in my LLM agent?▼

The best way to fix incorrect tool selection is to systematically evaluate your agent against test datasets to identify the root cause of errors. You can then implement targeted improvements to optimize tool selection accuracy and reduce inaccurate responses.

Does MLflow support end-to-end evaluation for LLM agents?▼

Yes, MLflow supports end-to-end evaluation for LLM agents. It provides native APIs covering the complete workflow, from initial tracing setup to evaluation execution, enabling robust performance tracking and observability for your agent workflows.

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

Yes, you need MLflow version 3.8 or higher to trace and evaluate LLM agents. Adhering to this version requirement ensures full compatibility with the native APIs required for datasets, custom scorers, and evaluation execution.

Can I use MLflow scorers to reduce the operational costs of my LLM agent?▼

Yes, you can use MLflow scorers to reduce operational costs. By systematically evaluating your agent's performance and identifying inefficient tool selections or poor answers, you can implement targeted improvements that optimize both cost and response accuracy.

Why does my LLM agent return inaccurate responses during tool selection?▼

Inaccurate responses during tool selection often stem from unoptimized logic or poor answer quality. Systematic evaluation using MLflow datasets and scorers helps identify these specific root causes so you can implement targeted agent improvements.

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