The Architecture hub defines an organization's worldview as the primary source of truth, treating all outputs—content, applications, and code—as [functional renders from worldview state] (Narrative_CompilationTargets). This framework relies on a [Git-centric architecture] (Narrative_RepoAsSourceOfTruth) where all content is committed prior to extraction, enabling append-only transactions and automated asynchronous processing. To maintain alignment, the system employs [version-controlled worldview evolution] (Narrative_BranchingWorldview), allowing strategic shifts to be managed with the same rigor as software changes. The model is guarded by [drift detection via recompiled stories] (Narrative_Telltales) and a [dual interface architecture] (Narrative_TwoInterfaceModel) that separates rapid chat-based iteration from structured, asynchronous collaboration. The core motivation remains the [prevention of training-distribution drift] (Narrative_WhiteSpace), ensuring that AI agents operate within the bounds of explicit organizational strategy rather than defaulting to generic training priors.
Witness phrases
verbatim functional transformation from worldview state to outputs
verbatim All content committed to Git before extraction
verbatim Worldview becomes branchable like code
verbatim Automatically recompiled stories serve as sensitive instruments for detecting worldview drift.
verbatim Without organizational worldview, AI agents default to training distribution
verbatim The model assumes a high-friction 'commit-first' workflow, which may conflict with needs for extreme low-latency, un-versioned experimentation.
verbatim The dual-interface model implies a potential fragmentation between 'fast' and 'async' knowledge, risking a disconnect between chat-based insights and the source-of-truth repo.