Evaluate and optimize AI agents through structured experiments
Microsoft Learn
A rare Microsoft course that treats agent evaluation as an engineering discipline, not guesswork.
September 14, 2026
Engineers building and shipping AI agents at scale
About
Covers methodologies for evaluating AI agent performance using measurable metrics, cost analysis, and systematic testing. Teaches how to design evaluation frameworks, apply Git-based workflows for reproducibility, and create consistent scoring rubrics. Focuses on evidence-based optimization decisions rather than ad-hoc tuning.
Who is it for
Intermediate engineers and MLOps practitioners who build or deploy AI agents and need to measure quality and performance systematically. Useful for teams optimizing agent behavior before production rollout.
Evaluation and optimization workflows
Bridges the gap between building agents and shipping them reliably. Emphasizes reproducible, Git-driven workflows and structured metrics over trial-and-error optimization.
What you learn
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