Huh. My definition - or rather, explanation - has always been, "The model is just a big bag of floats you multiply with some numbers to get some numbers out, plus a regular program that runs a loop which, at minimum, turns inputs (text, images) into a stream of numbers, pushes it through those multiplications against the bag of floats, and turns results back into text/images/whatnot. That regular program is called a harness[0]. Now, the trick to make LLMs into agents, is to add another loop in the harness that reads the output and decides whether to send it out to user, or do something else, like executing more code (that's what tools are), or feeding it back to input with some commentary (that's how you get "thinking"), or both (that's how you get the "agentic loop")".

Because there isn't really much more to it. And ever since we, i.e. those of us who played with ChatGPT API early on, bolted tools to it, some half a year before OpenAI woke up and officially named it "function calling" - ever since then, we knew that harness was the key. What kept changing was which logic (and how much of it) to put in explicitly, vs. pushing it back to the model on the "main thread", vs. pushing it to a model on a separate conversation track. But the basic insight remains the same.

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[0] - Well, today - until recently you'd call it a "runner" or "runtime".