Just starting to read this. Their Fig. 1 is basically the README of a project we've been quietly working on. It's easy to imagine how systems like these might

Just starting to read this. Their Fig. 1 is basically the README of a project we've been quietly working on. It's easy to imagine how systems like these might succeed by following workflows analogous to those humans use, on greenfield toy problems. The trouble is, if you paratrooper something like this into an exiting repository, or you want to build something of real significance, then you've got more context than can fit into an LLMs window. You don't just have the hallucination / stay-on-task problems, you have the problem of the LLM not having everything it needs to know to complete its task. In my experience, the latter problem of ensuring the LLM knows everything it needs to know is the bigger problem. The other stuff (using standard operating procedures and workflows, as in this paper) is actually the relatively easy stuff. I'm not saying it's easy-easy or that it's obvious from the outside if you haven't played around with it; I'm not disparaging the paper. So, what I'm most curious about, and I wonder if anyone here has seen good solutions, is this: How do these multi-agent systems maintain/retrieve the appropriate domain context required to make good design decisions, good coding decisions, and so on? "Use embedding and shove stuff into a vector database" is hand-wavy and doesn't get you all the way there. I'm looking for concrete solutions; academic papers that show a lot of promise; and the like. (Maybe they lay it out in this paper and I just haven't gotten to it yet. But if they've cracked that nut I suspect they would lead with it.)