People use "AI agent" and "LLM" like they mean the same thing, and that mix-up causes more confusion than it should. An LLM is the engine. An agent is what you build on top of that engine to actually get something done.
A large language model, ChatGPT or Claude in their plain chat form, is fundamentally reactive. You give it a prompt, it generates a response, and then it waits. It doesn't remember what it did five minutes ago unless that context is fed back into the conversation, and it can't take action in the world beyond generating text. Ask it to book you a flight and it can only describe how you'd do that yourself.
An AI agent takes that same underlying model and wraps it with the ability to plan, use tools, and act without a human typing every next step. Give an agent a goal like "find the cheapest flight to Chicago next Tuesday and book it," and it can search, compare, fill out the form, and confirm the booking, checking its own progress along the way instead of stopping after one response.
The practical difference shows up in autonomy. An LLM answers one question at a time and stops. An agent keeps a goal in mind across multiple steps, decides what tool or action comes next, and only stops once the task is actually finished, or it hits something it can't resolve on its own.
This distinction matters for anyone deciding what to actually build or buy. If you need a smart assistant to draft, summarize, or brainstorm, a plain LLM does that job fine. If you need something to complete a multi-step task with minimal supervision, booking, researching, filling out forms across several sites, that's agent territory, and it usually needs more oversight built in precisely because it's doing more without asking permission first.