The pivot framing is refreshingly honest. Most agent startups double down when the next model release makes half their scaffolding obsolete. Drift detection is the right insight though — you end up with more cleanup work than creation work once the agent output volume outpaces your review capacity. Curious how you handle the case where the "drift" is actually a real intent change the human hasn't formalized yet.

Each daemon runs in its own isolate, but the output is typically shared state; eg multiple daemons contribute to the same PR from separate container runtimes.

It’s possible to make naive daemons that stomp on each other (as with a UNIX daemon), but they’re highly responsive to coordination instructions and generally do very well at additive rather than competitive contribution.

I'm curious if evals of the DAEMONs and replays for debugging are on the roadmap?

I looked but did not see any facility for collecting/managing evals in the Charlie docs.

Docs drift might sound easy for agents but after working on it at https://promptless.ai for about two years, it's been tricker than just "make some skills". We've got an agent that watches PRs and suggests docs changes. Getting the suggestions good enough that doc owners would actually accept them took a fair bit of evals. Non-ai voice matching existing content, and even "simple" act of deciding whether a given PR warrants a docs change at all.

I have benefited greatly from evals catching things (especially as models change) to the point where I'm loath to go back.