From Rule Compliance to Value-Constrained Agency: A New Paradigm for AI Governance
Abstract
The rapid evolution of autonomous artificial intelligence from deterministic software to agentic systems has rendered traditional regulatory frameworks insufficient. Current governance models, largely rooted in static rule compliance and external oversight, struggle to keep pace with the emergent behaviors and non-linear decision-making processes of modern Large Language Models and multi-agent systems. This paper proposes a fundamental paradigm shift from externalized rule compliance to internal value-constrained agency. We argue that the central challenge of contemporary AI governance is not the absence of regulation, but the failure to address the structural and architectural dimensions of agentic intent. Through a comprehensive system-level analysis, we explore the trade-offs between system performance and normative alignment, the infrastructure requirements for robust value-embedding, and the socio-technical implications of deploying autonomous agents in critical infrastructures. We introduce a strategic framework for "governance-by-design," emphasizing the necessity of penetrating the internal reasoning traces of AI systems to ensure they remain tethered to human values even in novel, out-of-distribution environments. By synthesizing perspectives from systems engineering, public policy, and ethics, this research identifies the missing dimensions of current oversight and provides a roadmap for the institutionalization of value-constrained architectures. Ultimately, we conclude that the sustainability and resilience of the global AI ecosystem depend on our ability to transition toward a model where normative constraints are an intrinsic component of the agentic substrate, rather than a peripheral administrative hurdle.
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