What is Agentic AI, and how is it fundamentally differe...

The model underneath can be identical. What differs is the control flow wrapped around it. A traditional LLM application is a single pass — prompt goes in, completion comes out, and the application either shows that to a user or returns it from an API. Nothing evaluates the output against a goal or decides there’s more to do.

Agentic AI puts that same model inside a loop: after each output, the system asks whether the goal has been achieved, and if not, picks a next action — usually a tool call — and feeds the result back in for another pass. A request to “summarize this document” is one call and done. A request to “research a topic and write a report” might mean the system searches, reads, decides it needs more technical depth, searches again, drafts, notices a missing section, and only then stops. Same underlying LLM, but the second case has a decision loop sitting on top of it that the first doesn’t.

That loop is also what introduces the properties people associate with agents — self-correction, since a bad intermediate result can trigger a retry with a different approach, and tool use, since the loop is what decides when to reach for one. None of that comes from the model being smarter; it comes from something outside the model checking progress and choosing what happens next.