The Agent Loop: From Prompting to Agentic AI
You know how to write a good prompt. This short, free course adds the next idea: the loop that turns a one-shot answer into an AI that acts, checks its work, and keeps going until the goal is met.
If you already know prompt engineering, you know how to get a great answer in one shot. Agentic AI adds one deceptively simple idea on top: the loop. Instead of one prompt and one reply, the AI thinks, takes an action, looks at the result, and tries again — over and over — until the goal is reached. That single change is what separates a chatbot that answers from an agent that gets work done.
This is a short, plain-language, example-driven course. You will learn exactly what the loop is (think, act, observe, repeat), why the observe step is the whole point, and how looping lets an AI use tools and correct its own mistakes. Just as important, it is honest about the catch: loops can get stuck, wander, or run up cost, so you will learn the guardrails that make them reliable — step limits, stop conditions, and a human in the loop for anything risky. You will finish able to tell when a task needs a loop, when a single prompt is enough, and when a plain automation is the smarter, cheaper choice. No coding required. Tools and prices change fast, so the focus is on the durable concepts, with pointers to check current details yourself.
What you'll be able to do
- Explain the agent loop — think, act, observe, repeat — and why the observe step matters most
- Tell the difference between single-shot prompting, a fixed automation, and a looping agent
- Recognize why loops get stuck (repeating, wandering, runaway cost) and apply guardrails: step limits, stop conditions, and no-progress detection
- Keep a human in the loop for consequential actions, and know when a simple automation beats an agent
- Understand how tools like Claude Code, Cursor, and Devin combine prompting, a loop, tools, and a goal
Curriculum
Module 1: From a Prompt to a Loop
FreeSingle-shot prompting vs the agent loop, the four steps (think, act, observe, repeat), why the observe step is the whole point, and how prompting still lives inside every loop.
Module 2: Making Loops Reliable
FreeWhy loops get stuck, the guardrails that fix it (step limits, stop conditions, no-progress detection), keeping a human in the loop, and a full worked example of a research agent.
Module 3: Looping in Practice
FreeThe agentic tools that run loops for you, how to choose between a single prompt, an automation, and a loop, and a practical first looping task to try.
This entire course is free.
Every lesson is free — no purchase needed. When you're ready to go deeper for your job, field, or a specific skill, explore the full catalog.