I feel like there is some kind of information theory constraint which confounds our ability to extract higher order behavior from multiple instances of the same LLM.
I spent quite a bit of time building a multi agent simulation last year and wound up at the same conclusion every day - this is all just a roundabout form of prompt engineering. Perhaps it is useful as a mental model, but you can flatten the whole thing to a few SQL tables and functions. Each "agent" is essentially a sql view that maps a string template forming the prompt.
I don't think you need an actual 3D world, wall clock, etc. The LLM does not seem to be meaningfully enriched by having a fancy representation underly the prompt generation process. There is clearly no "inner world" in these LLMs, so trying to entertain them with a rich outer environment seems pointless.
I spent quite a bit of time building a multi agent simulation last year and wound up at the same conclusion every day - this is all just a roundabout form of prompt engineering. Perhaps it is useful as a mental model, but you can flatten the whole thing to a few SQL tables and functions. Each "agent" is essentially a sql view that maps a string template forming the prompt.
I don't think you need an actual 3D world, wall clock, etc. The LLM does not seem to be meaningfully enriched by having a fancy representation underly the prompt generation process. There is clearly no "inner world" in these LLMs, so trying to entertain them with a rich outer environment seems pointless.