I was going to say that this is how genetic algorithms work, but there is still too much human in the loop.
Maybe code husbandry?
Maybe code husbandry?
Imagine one agent just does docstrings - on commit, build an AST, branch, write/update comments accordingly, push and create a merge request with a standard report template.
Each of these mini-agents has a defined scope and operates in its own environment, and can be customized/trained as such. They just run continuously on the codebase based on their rules and triggers.
The idea is that all these changes bubble up to the developer for approval, just maybe after a few rounds of LLM iteration. The hope is that small models can be leveraged to a higher quality of output and operate in an asynchronous manner.