Or if they were just constantly prompted by outside stimulus. And if they could interact with the real world allowing them to observe cause and effect. In other words, if they were embodied.

I think you're 100% on the right track here. The key is memory, loops, and maybe a few other things like external interfaces which are just plain code and not deep learning voodoo. Many things do indeed run LLM's in a loop and attach external sources. See for example AutoGPT, the ReAct paper[1], and the Reflexion paper[2].

ReAct one line summary: This is about giving the machine tools that are external interfaces, integrating those with the llm and teaching it how to use those tools with a few examples, and then letting it run the show to fulfill the user's ask/question and using the tools available to do it.

Reflexion one line summary: This builds on the ideas of ReAct, and when it detects something has gone wrong, it stops and asks itself what it might do better next time. Then the results of that are added into the prompt and it starts over on the same ask. It repeats this N time. This simple expedient increased its performance a ridiculously unexpected amount.

[1] ReAct https://arxiv.org/pdf/2210.03629.pdf [2] Reflexion https://arxiv.org/pdf/2303.11366.pdf