AI assistance disclosure: This article was drafted with the help of Claude. All technical content, design decisions, code references, and screenshots reflect production systems I designed and operate at airCloset; the prose was revised by me prior to publication.
Hi, I'm Ryan, CTO at airCloset.
In the previous series, code-graph deep dive (Part 2), I wrote about making a 46-repo codebase semantically searchable for AI. The final issue I left open in that piece was the absence of dynamic analysis:
What lives on the graph is the fact that "this edge exists statically." How often that edge actually gets used in production isn't recorded.
A graph that gives you static facts is one thing. Telling AI what's actually happening in production right now is a separate problem. So the same shaping discipline I applied to the static graph needs to apply to the observability stack too.
