SORT-AI: Domain Architecture and Structural Diagnostics for Advanced AI Systems

Advanced artificial intelligence systems increasingly exhibit behaviors that are not adequately captured by component-local metrics, benchmark scores, or layer-specific monitoring. These behaviors arise across coupling surfaces, control regimes, deployment boundaries, and emergent interaction patterns, indicating that the relevant analytical object is the composed system rather than the isolated component. This article introduces SORT-AI as a canonical domain architecture for the structural diagnosis of advanced AI systems. The framework organizes the AI domain along four axes: Domain as the problem space, Cluster as the structural problem class, Application as a recurrent structural problem form, and Structural Dimensions V1 to V4 as the diagnostic grammar linking observed phenomena to structural causes, effect spaces, and decision surfaces. The current AI domain comprises 52 applications distributed across five clusters: Coupling, Learning, Control, Emergence, and Evidence. To make the domain paper self-contained, a compact mathematical basis is provided using a closed set of 22 idempotent operators, a global consistency projector, a calibrated projection kernel, and a structured projection space in which AI systems are read as operator chains on structured execution states. Runtime Control Coherence, represented by AI.04, is used as the canonical example to illustrate how locally correct control mechanisms can generate globally incoherent behavior under scale. The paper further incorporates SORT-Sovereign as a meta-domain that projects technical structural findings into strategic, regulatory, and state decision spaces. In this form, SORT-AI is positioned as a reusable scientific foundation for subsequent domain-specific analyses and application-level studies across the AI domain.