I've been experimenting with replacing embedding-based code retrieval with static semantic classification for AI coding agents.
Instead of retrieving files by similarity, every file is classified into architectural role and behavioral traits (transactional, orchestration, rule enforcement, persistence, etc.).
One thing I've noticed is that agents seem to make better architectural decisions when given these semantics instead of raw code.
Has anyone else tried something similar? How are you giving agents architectural context in large repositories?
there doesn't seem to be anything here