Hm. That looks a lot more granular, which is interesting... I'm not sure it would help me on this.

The codebase is small enough that I can basically go and find all the changes the LLM executed with each request, and read them with a very skeptical eye to verify that they look sane, and ask it why it did something or whether it made a mistake if anything smells wrong. That said, the code I'm rewriting is a genetic algorithm / evaluation engine I wrote years ago, which itself writes code that it then evaluates; so the challenge is having the LLM make changes to the control structure, with the aim of having an agent be able to run the system at high speed and read the result stream through a headless API, without breaking either the writing or evaluation of the code that the codebase itself is writing and running. Openclaw has a surprisingly good handle on this now, after a very very very long running session, but most of the problems I'm hitting still have to do with it not understanding that modifying certain parameters or names could cause downstream effects in the output (eval code) or input (load files) of the system as it's evolving.