I might be missing something?
EDIT: I should add that the first step is used to cut down on the number of function definitions I need to send to the model on each user prompt. Navigating a map can be done with as few as four function definitions but styling a map gets out of control fast (google "Mapbox Style Specification" if you want to see why).
https://www.amazon.com/PolyScience-Temperature-Controlled-Co...
Although now that I think about it, a lot of doctors practices have a MyChart-style portal where you can schedule an appointment yourself. Why does an LLM need to be involved in that process? I guess for people who still want to schedule over the phone, the LLM agent makes sense. Kind of, assuming you don't have any special case problems. Which patients most likely do, if they're calling in. Is an LLM actually a good solution here?
To clarify more, I see frameworks like CrewAI and similar, with tools even from Microsoft to define these “agents” quickly. But when I tried them, I noticed they are no more than chain of thought CoT functions to ask/extract/generate based on user input and functions output.
As such, they can be quite unpredictable, hence my question of examples of LLM agents being used in production. I just don’t see their value, but I might be missing something so wanted to see examples to understand more.