Catalog listing Microsoft Learn module

Evaluate and optimize AI agents through structured experiments: Review

Named reviewer score 4.2 / 5
  • Teaching 4.0
  • Hands-on 4.0
  • Freshness 5.0
  • Value 4.5
  • Career fit 4.0

Provider: Microsoft Learn Format: Self-paced Microsoft Learn module Time: ~1 hour Price: Module is free; check credential exam costs if attached. — Check current pricing on the official site.

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Reviewer's verdict

This is the missing manual. It's the first useful thing I've seen on moving from a 'cool' agent demo to something you'd actually ship. It replaces wishful thinking with a real process.

Decision summary

4.2/5 Last verified: August 31, 2026

Best for

Developers who have built a basic agent and are now asking 'Is this thing any good?'

Watch for

Assumes you're bought into the Microsoft Foundry ecosystem.

  • Teaching 4.0
  • Hands-on 4.0
  • Freshness 5.0
  • Value 4.5
  • Career fit 4.0

Who it's for

Developers who have built a basic agent and are now asking 'Is this thing any good?'

Who should skip it

Absolute beginners or anyone looking for a theoretical deep dive on statistical evaluation.

Pros

  • Extremely practical, Git-based workflow for agent evaluation.
  • Introduces concrete metrics beyond simple accuracy.
  • The exercise is a realistic, albeit small-scale, simulation of the problem.

Cons

  • Assumes you're bought into the Microsoft Foundry ecosystem.
  • The scoring rubric example is a good start but needs more real-world nuance.
  • A bit short; feels like the first chapter of a much larger book.

This module finally answered the question that’s been bugging me for months: how do I know if my agent is getting better, or just different? I've been hacking on a small RAG project, and my 'evaluation' was just me asking it questions until I got a bad answer. It felt stupid. This module lays out a proper engineering discipline for it.

The Git-based workflow alone was worth the hour. Instead of messing with my main branch, I can now create a test branch, tweak a prompt or a model, run a batch of test cases, and compare the outputs side-by-side. It’s structured. It’s repeatable.

The exercise has you write a scoring function. It’s a simple pass/fail based on keywords, which is a bit basic for production, but it forces you to think in terms of automated metrics, not just vibes. It introduces concepts like 'groundedness' and 'relevance'—finally, a vocabulary to describe *why* a response is bad, instead of just saying 'it hallucinates'.

It’s not perfect. The whole thing is clearly a gateway to get you to use Microsoft Foundry. Fair enough. And the scoring could be more advanced; real-world evaluation needs to handle nuance, not just keyword matching. But it gets you 80% of the way there, from zero to a repeatable, evidence-based process. It’s less of a 'course' and more of a standard operating procedure I can now apply.

Last verified: August 31, 2026. We re-check course details periodically — see our methodology. Pricing and availability can change; always confirm on the official site.

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