AI Agents & LLMs 2026: Evaluating & Testing AI Agents

About this course

This course contains the use of artificial intelligence.

That is not a disclaimer — it is part of the point. The slides and the voice narrating the lectures were produced with AI tools, but every script was written and fact-checked by a human instructor with real hands-on experience. Think of the AI as the production crew; the teaching is human.

Your agent works. In the demo. But how do you actually know it works — across the messy, unpredictable range of things real users will do?

Agents broke the old testing playbook. Same input, different output. No single right answer. Failures a compiler never sees.

This course is the discipline that answers the question.

What you will learn:

• Evaluating and Testing AI Agents brings the tester's mindset to a non-deterministic world.

You'll understand how to use an evaluation set and choose the right metrics. You'll use LLM-as-judge without fooling yourself. You'll test the parts a final answer hides — tool use, retrieval, groundedness, and the full multi-step trajectory. And you'll learn how to ship with confidence, using observability, regression testing, red-teaming, and production monitoring.

What this course covers, module by module:

Why Testing Agents Is Different; What Can Go Wrong; The Evaluation Mindset; Building an Eval Set; Reference-Based Metrics; LLM-as-Judge; Testing Tool Use; Testing RAG & Groundedness; Testing Multi-Step Agents; Observability & Tracing; Regression Testing & CI; Human-in-the-Loop & Red-Teaming; Production Monitoring.

Who this is for:

This is for anyone building agents who's stuck on "I can't tell if my changes are making it better or worse" — and for QA people who want their discipline to translate.

Because how do you know it works?

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