Have you tried using a more traditional, non-LLM, loop to do the analysis? I'd assume it wouldn't catch more of the more complex deceptive behaviours, but I'm assuming most detections can be done with various sentiment analysis / embedding tools which would drastically reduce cost and latency. If you have tried, do you have any benchmarks?

Anecdotally, I often end up babysitting agents running against codebases with non-standard choices (e.g. yarn over npm, podman over docker) and generally feel that I need a better framework to manage these. This looks promising as a less complex solution - can you see any path to making it work with coding agents/subscription agents?

I've saved this to look at in more detail later on a current project - when exposing an embedded agent to internal teams I'm very wary of handling the client conversations around alignment, so I find the presentation of the cards and the violations very interesting - I think they'll understand the risks a lot better, and it may also give them a method of 'tuning'.