Summary

You can now explain why an AI-native org treats content as one graph rather than a collection of silos - with Cogitave's estate as the worked example - and how that graph stays honest as it grows.

In this module, you:

• Learned the thesis, worked through Cogitave's Core architecture spec: docs, the IDP, governance, and infra are projections of one graph, never separate stores.
• Traced how a source document becomes a node: the ACQUIRE-to-PUBLISH pipeline, the UID/contentHash identity scheme, and the closed edge set (xref, partOf, appliesTo, teachesSkill) that links it in - served identically to humans and agents through docs_fetch and the cogitave://{type}/{id} resource surface in the MCP interface.
• Learned why linked edges are not the whole propagation story: Cogitave's knowledge-propagation standard names restatement drift and fixes it with a fact registry - one owner document per fact, everything else cites - checked by a deterministic fact-drift scanner.
• Read the status of Cogitave's Core honestly: it is a specification and architecture today; the registry and scanner already run over files on the mirror, and full graph projection lands when Core runs.

You have completed the path

This was the final module of Build on a single canonical model. Completing it earns the path's trophy - you now have a working model of the one graph every product, standard, and agent in an AI-native estate shares (Cogitave's own being the worked example), and how to keep it truthful as content grows.

Where to go next

• Build on a single canonical model - return to the path to claim your trophy.
• The Core architecture doc, in Cogitave's core repository, is worth a re-read for the full node/edge model and the change pipeline at the source.
• The knowledge-propagation standard is the canonical reference for the fact registry format, mention rules, and the impact-map skill in full.