Architectures

This section presents the structural models and execution frameworks derived from the foundational axioms. Here, semantic principles are translated into operational mechanisms, forming the basis of AI execution environments, agent interfaces, and organizational coordination systems.

Architectures in this section define:

How semantic intent becomes executable structure
How agents evaluate, transition, and complete tasks
How delegation and responsibility are represented in runtime conditions
How a semantic instruction layer mediates natural language and computation

These documents describe not what a system does, but how it must be constructed for its behavior to remain verifiable and accountable.

Architectural AI vs Model-centric AI

Artificial intelligence is increasingly conflated with machine learning models. This framing represents a category error. Architectural AI refers to intelligent systems whose core capabilities—adaptation, awareness, and evolution—emerge from system architecture rather than from any single machine learning model. In this paradigm, ML-based models, including large language models, are treated as inference modules, not as artificial intelligence itself. This paper addresses system-level intelligence, not task-level performance. Social Adoption and Agency For artificial intelligence to be socially adopted and institutionally governed, strict distinctions must be maintained. ...