It’s the basic rhythm behind most agentic work: an agent keeps going after the first answer because the job isn’t finished yet, not because it’s confused. A good loop has a task, boundaries, and a clear stopping point.
Think about an assistant working through an inbox. They open an item, decide what it needs, act, look at what happened, and decide whether it’s done or needs another pass. Then they move to the next one. They’re not following a frozen script. They’re checking, acting, and adjusting.
How it shows up
We explain it in training with a simple pattern: check my email every fifteen minutes until Landry sends the deck, then notify me and turn it into slides. The agent checks, sees nothing, waits, checks again, sees the event, and moves on. Review work is similar: build a deck, score it against a rubric, fix it, and repeat until it passes. Inside the loop the agent may make a tool call to read a file, search a repo, run tests, or hit an API, then update its plan from the result. You can even run cross-model review inside the loop, where one model drafts and another reviews before a human takes the final call.
Why you care
Guardrails are what keep a loop from spinning: wasting time, hitting rate limits, or retrying a broken assumption. Design the loop like delegation. What’s the task, what’s the standard, which tools can it use, and when does it stop or ask you? Real work rarely finishes in one pass. The loop is what lets the agent take the rest.