probably what will work best in the future is specific training or fine-tuning against curated datasets of narrative fiction and/or texts in general? not really sure.
I will confess however that some people internally find the model confusing. For example, one user couldn't remember that to get an issue actioned, they needed to put it into the "waiting room". They wanted to be able to action their issues without learning how complex medical systems operate. There seems to be more work on our plates to make this resonate with all users.
One thing I've been worrying about recently is the notion of comprehension debt and making sure the humans can still understand the system (so as to be able to do incident response or something if the LLMs are down). The slower, more methodical workflow described here should definitely help by allowing checks like proper docs whenever a piece of the architecture changes for instance, but I'm curious what else could be done to help the humans understand as much of the implications of what the AI is doing as possible.
Edit: having the code author and reviewer be completely separate like you described below is also probably pretty helpful for making sure changes are comprehensible from the outside.
i kinda want to try to build this off of github. it's essentially just an event bus / message queue that workers (agents) tap into.
That type of encoding (starting with the statement: you're at a teaching hospital) is profoundly more efficient than having to deeply and reliably articulate what each of the various roles your agents embody are, let alone their interactions.
Perhaps there is more room for any of us to consider what existing systems or identities are described abundantly in training sets and can be leveraged to ritualistically encode these social/civic/cultural dynamics saying: "act as though you're ___." Not exactly a new point, but one this write-up certainly is pulling me towards.
Thank you for sharing and the care you put into writing this!