A chatbot finishes when it answers your question. But a request like "find and fix issues until this project's tests pass" cannot end with one answer.

That is where repetition, or a loop, comes in. AI observes the current state, acts, checks the result, and picks the next action.

Why do you need loops?

Most real work does not finish in one shot. After a code edit you must read test results. After research you must check what is missing. The next move depends on the result.

Simplified, it looks like this:

Check state → Act → Check result → Pick next action → Repeat

The point is not "spin forever." You also need rules for when to continue and when to stop.

Without stop rules, agents chase noise. With them, repetition becomes progress.

Good repetition has exit conditions

A loop alone does not make a good agent. With a vague goal, AI can grind on needless work or repeat the same error.

So when you design a loop, separate at least these three:

  • What is the current goal?
  • How will you verify the result is good enough?
  • Under what conditions will you stop?

The clearer these three get, the more repetition turns from blind retry into goal-directed work.

Time, cost, and attempt caps also help. They force the agent to ask for help instead of looping quietly.

The difference between prompts and loops

A good prompt improves one judgment. A loop connects many judgments over time. So writing prompts well and designing repetition are different problems.

If agents are new to you, start here before complex multi-agent setups: "one agent repeating one goal." That pattern is far easier to grasp.

Prompts raise single-step quality. Loops raise task-completion quality.

What matters is feedback, not repeat counts

A loop's value is not retrying alone. It is feeding the last result into the next decision. Feedback-carrying repetition is the core.

Once you see this, coding agents, research agents, and automation workflows start to look alike. They all observe, act, verify, and adjust.

Log each turn's observation and decision. That trace turns a black-box loop into something you can debug.

References

The bottom line: add feedback and a stop rule this week

Pick one repeating task and write its goal, check, and stop condition on paper. When results feed the next move and stopping is explicit, your loop starts behaving like an agent.

Go deeper with a course

If you want to turn simple loops into reliable agent workflows with harnesses and graph structure, practice on a real project.