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agent-evaluation

Evaluate and improve LLM agent output quality using MLflow APIs.

1|1|Updated Nov 9, 2025
Search Tags:#testing#quality assurance#llm#agent#evaluation#mlflow
One-click install
npx skills add https://github.com/Paldom/databricks-apps-streamlit-vibe-coding-starter --skill agent-evaluation-paldom
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/Paldom/databricks-apps-streamlit-vibe-coding-starter/tree/main/.agents/skills/agent-evaluation
Command: npx skills add https://github.com/Paldom/databricks-apps-streamlit-vibe-coding-starter --skill agent-evaluation-paldom

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of 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 workflow of evaluating agents using MLflow, from setup to analysis.
  • Component-Specific Evaluation: Allows for focused evaluation of individual components like tracing setup, dataset creation, or scorer definition.
  • Use Case: You've deployed an LLM agent for customer support, but its responses are sometimes inaccurate. Use this Skill to systematically evaluate its performance against a dataset of customer queries, identify weaknesses, and implement improvements.

Quick Start

Use the agent-evaluation skill to set up MLflow tracing for your agent and run an initial evaluation.

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 systematically?▼

To evaluate LLM agent output quality systematically, you can use MLflow's native APIs to run an end-to-end evaluation workflow covering tracing setup, dataset creation, scorer definition, and evaluation execution.

What is the best way to set up MLflow tracing for an LLM agent?▼

Setting up MLflow tracing for an LLM agent involves using MLflow's native APIs to establish observability, which is a core component of the agent evaluation workflow for ensuring reproducible and measurable outputs.

Can I evaluate individual components of my LLM agent instead of the entire workflow?▼

Yes, you can evaluate individual components of your LLM agent, such as focusing specifically on tracing setup, dataset creation, or scorer definition, rather than running a complete end-to-end evaluation workflow.

Do I need a specific MLflow version to run LLM agent evaluations?▼

Yes, you need MLflow version 3.8 or higher to run LLM agent evaluations, as the process requires strict API usage for observability and reproducibility.

Why does my LLM agent evaluation require custom scorer definitions?▼

Custom scorer definitions are required to systematically measure and improve the accuracy, cost-effectiveness, and reliability of your LLM agent against a dataset of queries.

What limitations exist when using MLflow for LLM agent evaluation?▼

The main limitation is the strict requirement for MLflow 3.8+ and adherence to strict native API usage for observability and reproducibility, which may constrain custom evaluation framework integrations.

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