AI Agents: Build Your Mental Model

Everyone’s talking about AI agents. Almost no one can tell you what one actually is — or when you should use one.

If you’ve ever nodded along to a conversation about “AI agents” while quietly wondering what really separates an agent from an ordinary chatbot, or whether your project even needs one — this course is for you.

It’s a concept-first, no-code course that gives you something most AI content skips entirely: a clear mental model of how agentic systems actually work, and the judgment to decide when to build an agent, when a simpler workflow wins, and when you need neither.

What you’ll walk away able to do

• Classify any task as a workflow, an agent, or a hybrid — and explain why.
• Spot which capabilities a system actually needs: tools (to act), retrieval (to know), memory (to persist).
• Right-size autonomy so you never over-build a simple problem into an expensive, unreliable one.
• See how it all fits through a single real example, built two different ways.

How the course works

In about 45 minutes of short, visual lessons, we build the mental model, one idea at a time. You’ll learn the one question that defines every system you’ll ever build — who decides the next step? — meet the “augmented LLM” and its three capabilities, and internalize the single most useful principle in the field: autonomy is a cost, not an upgrade. Then we take one real task — a refund assistant — and evaluate it as a workflow and as an agent, so you see every concept in action and understand exactly which to ship.

You’ll finish with downloadable practice exercises, a one-page cheat sheet, and a glossary so the model sticks long after the last video.

No code. No setup. No jargon for its own sake.

You don’t need to be a programmer, and you won’t write a single line of code. This course works at the level where projects are actually won or lost — the design decisions you make before anyone starts building.

A note on scope (so you know exactly what you’re getting)

This course makes you fluent in the design decision — what to build and whether to build it. The engineering that follows (cost, performance, integration, and actually shipping to production) is a deliberate next stage, not part of this course. Getting the design right first is the highest-leverage thing you can do — because the most expensive mistake in AI isn’t building slowly, it’s building the wrong thing well.