"My students can chat with AI. I have no idea how to teach them to build one." If you run a CS classroom, a makerspace, a robotics team, or a hackathon, you know the gap: AI literacy for CS teachers has mostly meant slides and vocabulary, and there is no hands-on agent curriculum for the one skill the field now rewards, building and governing autonomous agentic loops.

The Loop Lab is that curriculum. Across a tested, term-length sequence of buildable labs, your students assemble an agentic loop from scratch, deliberately watch it drift off-goal and burn tokens, then engineer the guardrails that make it trustworthy. You teach the failure first and grade the judgment after.

Inside, teaching AI agents in the classroom becomes a lab you can actually run:

The build-break-stop lab structures every session: surface a failure students can see, then teach the one guardrail that fixes it.
A runaway demo that lands every time, so the class watches a live loop drift and burn before anyone touches a fix.
A drop-in grading rubric for every lab, built on evals and verification, so you grade the judgment instead of the vocabulary.
Project-based AI lessons in a proven sequence, field-tested across classroom and hackathon sessions before they reached this page.
Agentic loop lesson plans ready Monday morning, from the first assembled loop to the engineered stop.

Read it and you will close the term with students who can build a loop, prove it works, make it stop, and explain when a loop is the wrong tool entirely, the durable judgment no model upgrade erases. This is one of the first hands-on agent curricula written for the classroom, by a working learner-builder who teaches the way the field actually advances: ship, break, verify, repeat.

For CS teachers, makerspace and robotics mentors, hackathon coaches, and instructors of advanced high-school and undergraduate courses who can teach building but have no curriculum for proving and stopping a loop. Part of the Build Agents You Can Trust series, in The Verifier's Library.

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