AI coding agents have an expensive habit: before they write a single line, they re-read source files to work out what already exists — which modules there are, what each one provides, what's tested, and what's currently being changed. On a small repo that's tolerable. Run several agents in parallel on one codebase and it becomes both a token sink and a coordination problem: two agents start the same feature, a test gets added that nobody can map to a requirement, an architectural decision made in one session is invisible to the others.
I kept hitting this running multiple Claude Code sessions against a single codebase, and ended up solving it the way you'd expect on a graph-shaped problem: model the codebase's requirements chain as an ontology in Neo4j, and let the agents query the graph instead of re-reading the source.
This post is about the data model and the queries — and why a graph is the right tool here rather than a table.
The model
The core idea is full requirements traceability, from a user story down to the unit test that verifies a routine. Every artefact is a node; the relationships carry the meaning.
