AGI Stability Conditions A Structural Framework for Axis Closure and Bounded AGI Artificial General Intelligence is often framed as a scaling problem of model capability. This paper argues that stable AGI is not a function of scale, but of architectural closure across four structural axes: Generalization Persistence Bounded Autonomy Temporal Awareness We define Axis Closure Conditions (ACC1–ACC4) and prove a Structural Instability Theorem: If any axis remains open, bounded AGI stability fails. Axis 3 (Bounded Autonomy) is shown to be structurally equivalent to S3 governance stability, as defined in: Bojanowski (2026), Deterministic Dual-Gate Governance for Agentic AI. Minimal Stability Conditions and Practical Enforcement Architecture DOI: https://doi.org/10.5281/zenodo.18652217 Thus: ACC3 \iff S3 This establishes a formal bridge between governance enforcement architecture (Chimera) and structural AGI stability theory. The framework is: Architectural rather than cognitive Falsifiable rather than speculative Compatible with deterministic governance enforcement Independent of ontological assumptions about consciousness The paper further: Provides empirical grounding for persistence (ACC2) and temporal closure (ACC4) Distinguishes binary (governance) vs gradient (capability) closure Defines instability classes for open axes Argues that stable AGI is necessarily bounded This document constitutes the structural theory layer within the broader Alliance Research Group (ARG) research program.