Most companies experimenting with AI agents are stuck in the same place. They build a pilot, run a few tests, and then the project stalls.

Not because the technology is too new or too complicated, but because most of these projects are built without a clear plan before the technical work starts.

We’ve worked with thousands of teams that have built and deployed agents. The patterns are consistent. The ones that succeed follow a checklist of fundamentals, while the ones that fail skip them.

This course is a set of practical decisions that every successful AI project makes early on.

To make it easier to follow, we’ll use a fictional company: Terminal Roast. It’s a small coffee brand that operates a café and sells coffee beans online. They want to use an AI agent to improve their customer experience.

We’ll follow their story throughout the course and see what each step looks like in practice.

Every lesson in this course is built around a foundational decision for an AI agent project:

• Choosing a single, well-defined task
• Picking the right channel for deployment
• Knowing when something truly needs an agent
• Bringing the right people into the project
• Defining an LLM strategy
• Establishing compliance and safety measures
• And finally, deciding what success looks like after launch

If you can answer each of these questions with clarity, you’re already ahead of most teams trying to ship their first agent.

Let’s think about Terminal Roast.

Taryn, the owner, is excited about using AI to improve the business. She has a long list of ideas.

She wants an agent that can handle order-ahead pickup, loyalty programs, and customer support.

But her team quickly realizes that’s a bit too ambitious for a first project.

Over the next few lessons, we’ll follow how Taryn and her team make the project more realistic.

We’ll see how they scope down the project in order to get a real agent working.

The purpose of this course is to help you make better decisions before you build.

By the end, you’ll have a complete checklist used by teams that consistently ship production-ready agents.

Each lesson ends with a clear action you can take right away to move your project forward.

Action: Write down one AI project your team has discussed. Then, write a sentence explaining the business problem it’s meant to solve.

If you can’t summarize the problem clearly, take another look before moving forward.