The one-model architecture
Explain why an AI-native organization runs on one canonical property graph that humans and agents both query, what its node/edge model and identity scheme look like, and why a labeled property graph is the right substrate. Cogitave's Core is the concrete example.
Units5
Duration24 min
Levelintermediate
By the end of this module, you'll be able to:
- Explain the one-model thesis: why a single canonical graph that documentation, the IDP, governance, infrastructure, and agents all project from beats one store per domain.
- Describe the canonical graph itself: its node and edge types, its two orthogonal identifiers (
uidandcontentHash), and the three materializations served from it - using Cogitave's Core as the worked instance. - Explain why the substrate is a labeled property graph specifically, and why the relational, RDF, and federated alternatives are rejected (the reasoning Cogitave's ADR-0001 records).
- State accurately what such a model decides today versus what is architecture to build against, without overstating a running product.
Prerequisites
- No prior experience with a canonical model required.
- Helpful, but not required: completion of the "Onboard to an AI-native codebase" path.
Units
- 01Introduction4 min
- 02The one-model thesis7 min
- 03The property-graph substrate7 min
- 04Knowledge check3 min
- 05Summary3 min
Related
- A native MCP interface to the canonical model Read a canonical model's native MCP interface as the source of truth for how humans and agents query one org's estate - why the surface is native rather than adapted, the protocol contract it commits to, and the tools and resources you call. Cogitave's Core is the worked example.
- Project products into the canonical model See how content projects into one canonical model as typed, content-addressed nodes rather than separate silos - Cogitave's Core is the worked example - and how the fact registry's cite-not-restate rule and fact-drift scanner keep one owner per fact across your estate.
- Query your canonical model Learn how a query layer resolves a request against one canonical graph - lexical BM25 and dense HNSW vectors fused by Reciprocal Rank Fusion, a graph-aware rerank and ranking signals, and a bounded, read-only profile agents call directly. Cogitave Query is the reference implementation you'll trace.