Most people think of AI as a single exchange: you ask, it answers. But agentic AI works differently — it runs in a loop, repeatedly taking actions, observing results, and deciding what to do next until a goal is met.

This reasoning loop is what separates a basic chatbot from a true agent. Instead of stopping after one response, the agent can call tools, check its own output, course-correct, and try again — sometimes dozens of times before finishing a task.

The tradeoff is real: loops are powerful but unpredictable. Each iteration can introduce errors that compound, and longer loops cost more compute. Knowing when to stop — and whether the result is trustworthy — is one of the core unsolved challenges in building reliable agents.