Your first looping task

You now understand the loop, its failure modes, its guardrails, and when to use it. Time to run one. The goal of this final lesson is a single, safe, well-scoped looping task you actually do — because watching a real loop turn is what makes all of this click.

Pick a read-and-draft task (safe by design). Choose something where the agent only reads, searches, and drafts — never sends, pays, or deletes. Good first tasks: "research the best approach to X and write me a short sourced summary," or, in a coding agent, "find and fix the failing test in this small project," or "read these three documents and produce a one-page comparison." All are multi-step, all benefit from the agent checking its own work, and none can cause harm because nothing consequential happens automatically.

Scope it with the recipe from Module 2. Before you start, write the six things: the goal, the stop condition (how you will know it is done), a rough step limit in your head, what should make it stop and ask you, the minimum powers it needs (read and draft only), and the point where you approve the result before using it. Even a loose version of this keeps the run on track.

Run it and watch the loop. Use one of the tools from earlier — a coding agent, an assistant deep-research mode, or a no-code builder. As it works, watch for the four steps: notice it think, take an action, observe a result, and repeat. Seeing the cycle in a real tool is the payoff of this course — the abstract idea becomes something you can point at.

Check its work — do not trust it blindly. When it finishes, verify: are the sources real, does the fix actually pass the tests, is the comparison accurate? The observe step gives the agent self-correction, but you are the final check. This habit — let the loop do the work, then verify before you rely on it — is the core discipline of using agentic AI well.

Where to go from here. Once you are comfortable running safe read-and-draft loops, you can graduate to loops that take actions (with approval on the risky steps), and to the deeper courses on building reliable agents, grounding them in your own data, and connecting tools. But the mental model you now have — think, act, observe, repeat, kept in check by guardrails and a human — is the foundation under all of it.

Recap. Run one real loop to make it click: pick a safe read-and-draft task (research and summarize, fix a failing test, compare documents) where nothing consequential happens automatically; scope it with the six-part recipe (goal, stop condition, step limit, stop-and-ask rule, minimum powers, your approval point); run it in a looping tool and actually watch it think, act, observe, and repeat; then verify the result yourself before relying on it. Let the loop do the work and be the final check — that discipline, plus the think-act-observe-repeat model kept in line by guardrails and a human, is the foundation for everything agentic you will build next.