An agent loop is the repeated cycle that lets an AI system keep working instead of stopping after one answer. It can think, act, check the result, and continue. This is what makes agents useful for tasks like searching, comparing, fixing, and retrying without constant human prompting.

An agent loop is the engine behind agent behavior. Instead of doing one response and ending, the system repeats a cycle: understand the current state, decide the next action, use a tool or generate an output, observe what happened, and then continue if needed. This loop is what allows an agent to complete multi-step work such as gathering information from several places, correcting its own errors, or trying a second approach when the first one fails. Without a loop, you mostly have a single-turn assistant. With a loop, you have a system that can move toward an outcome. The key design challenge is knowing when to continue, when to stop, and when to ask for human approval so the loop stays efficient and safe.

Why Agent Loops Matter