Focus on this objective today: Evaluate agent performance: Create a test set. Explain it in your own words, apply it to one realistic scenario, and verify the details against the official source. Source context: Read in English Evaluate and optimize AI agents through structured experiments Module 7 Units Feedback Intermediate AI Engineer Developer Solution Architect Azure Microsoft Foundry Learn how to optimize AI agents through structured evaluation that transforms guesswork into evidence-based engineering decisions. You'll explore how to design evaluation experiments with clear metrics for quality, cost, and performance; organize experiments using Git-based workflows; create evaluation rubrics for consistent scoring; and compare results to make informed optimization decisions. Learning objectives In this module, you: Design evaluation experiments with clear metrics for quality, cost, and performance Apply Git-based workflows to organize and compare agent variants systematically Create evaluation rubrics that ensure consistent scoring across human evaluators Compare experiment results to make evidence-based optimization decisions Add Prerequisites Before starting this module, you should have: Basic understanding of AI agents and large language models Familiarity with Git version control workflows Experience with Microsoft Azure AI Foundry or similar AI development platforms Get started with Azure Choose the Azure account that's right for you.
Objective
Evaluate agent performance: Create a test set