Toward an Internal Governance Architecture for General-Purpose AI Systems

Abstract

The rapid evolution of general-purpose artificial intelligence (GPAI) has transitioned from narrow task-optimization to expansive, autonomous agency, creating a governance vacuum that traditional external regulatory frameworks are ill-equipped to fill. Current governance models rely heavily on post-hoc filtering, application-level constraints, and reactive policy enforcement, which treat the AI system as a black box. This paper argues that such externalized governance is structurally insufficient for managing the emergent risks of deceptive alignment, goal drift, and systemic brittleness inherent in high-dimensional agentic systems. We propose a transition toward an Internal Governance Architecture (IGA) that embeds normative constraints, safety protocols, and accountability mechanisms directly into the system’s latent reasoning layers and architectural substrate. By synthesizing perspectives from systems engineering, socio-technical infrastructure, and the foundational necessity of internal reasoning traces, this research explores the feasibility of accountability-by-design. We analyze the structural trade-offs between computational efficiency and the depth of internal monitoring, the sustainability of deploying resource-intensive auditing frameworks at scale, and the policy implications for global alignment standards. The discussion emphasizes the necessity of penetrating the internal logic of autonomous systems to ensure fairness and robustness in multi-agent ecosystems. We conclude by providing a roadmap for the institutionalization of internal governance, suggesting that the stability of the future socio-technical landscape depends on our ability to govern the intelligence of AI from the inside out, moving beyond superficial constraints to a deeper dimensional integration of oversight.