Functional Self-Awareness Architecture for Regulated Autonomous Multi-Model AI Systems

Modern AI systems orchestrate language models, retrievers, planners, and external tools, yet lack structured mechanisms for self-representation and behavioral governance. System-level failures arise not only from reasoning errors but from miscalibrated confidence, silent degradation, and unregulated action authority. We introduce a Functional Self-Awareness Architecture for multi-model AI systems, comprising four integrated structural components: (1) a persistent self-model (Σ) representing system identity, boundaries, and operational history; (2) uncertainty-awareness mechanisms (U) generating reliability signals contextualized by declared authority; (3) a meta-cognitive evaluation layer (E) monitoring system state independently of task execution; and (4) self-governance interfaces (G) enforcing enforceable constraints on action authority. The proposed framework establishes Functional Self-Awareness Architecture as a substrate-agnostic methodology for constructing internally regulated AI systems capable of recognizing and governing their operational boundaries. The object (substrate) layer performs task reasoning while a meta layer (agnostic) integrates identity state, uncertainty, and performance signals to modulate authority without altering task computation. We formally derive minimal architectural conditions required for stable reflexive regulation and conduct structured failure-mode analyses across language-model pipelines. Results demonstrate that removal of any condition produces distinct, scale-invariant governance vulnerabilities.