The beauty of LLMs isn’t just these coding objects speak human vernacular but they can be concatenated with human vernacular prompts and that itself can be used as an input, command or output sensibly without necessarily causing error even if a series of inputs combinations weren't preprogrammed.
I have an A.I. textbook that has agent terminology that was written preLLm days. agents are just autonomous ish code that loops on itself with some extra functionality. LLMs in their elegance can more easily out the box selfloop just on the basis concatenating language prompts, sensibly. They are almost agent ready out the box by this very elegant quality(the textbook agentic diagram is just a conceptual self perpetuation loop), except…
Except they fail at a lot or get stuck at hiccups. But, here is a novel thought. What if an LLM becomes more agentic (ie more able to sustain autonomous chain prompts that do actions without a terminal failure) and less copilotee not by more complex controlling wrapper self perpetuation code, but by means of training the core llm itself to more fluidly function in agentic scenarios.
a better agentically performing llm that isnt mislabeled with a bad buzzword might not reveal itself in its wrapper control code but through it just performing better in an typical agentic loop or environment conditions with whatever initiating prompt, control wrapper code, or pipeline that initiates its self perpetuation cycle.