AI Knowledge Architecture

AI Knowledge Architecture proposes an architectural model for converting accepted AI interpretations from transient probabilistic inference into persistent, explicit knowledge structures. The work applies the principle of Engineering Determinacy to AI knowledge representation: once an interpretation has been explicitly established and accepted, it should not need to be repeatedly re-inferred. The architecture introduces a unified determinacy stack consisting of persistent Claim Objects, semantic Attributes, grounded and qualified Relations, persistent Viewpoints, and dynamically derived Viewpoint Domains over a shared knowledge state. The proposed architecture does not replace Retrieval-Augmented Generation (RAG), knowledge graphs, provenance models, semantic units, or ontology views. Instead, it addresses a distinct architectural problem: how accepted semantic interpretation can persist beyond an individual inference cycle and become reusable governed knowledge. The paper also distinguishes probabilistic knowledge discovery from structure-governed reuse through an explicit admission boundary, and illustrates how multiple analytical Viewpoints can organize the same shared Claims and Relations without duplicating the underlying knowledge state. Core design principle:Once an interpretation has been explicitly established and accepted, it should not be re-inferred. This paper provides the architectural foundation for subsequent work on Knowledge Evolution, including knowledge construction, integration, reconstruction, and exploration.