AI Governance in the Era of Agentic Generative AI and AGI: Frameworks, Risks, and Policy Directions

The accelerating development of agentic artificial intelligence (AI) and the prospect of artificial general intelligence (AGI) create unprecedented opportunities alongside complex governance challenges. This paper examines the ethical, regulatory, and technical dimensions of governing highly autonomous AI systems, drawing upon more than fifty contemporary academic and policy sources. Three core insights emerge. First, current governance structures provide limited coverage of risks linked to recursive self-improvement and multi-agent coordination, with only an estimated 10–15% of safety research addressing impacts that arise after deployment. Second, economic projections suggest that agentic AI could generate between 2.6 and 4.4 trillion USD in added global output by 2030, yet automation could replace approximately 28–42% of existing job tasks, making proactive workforce transition strategies a policy necessity. Third, fragmented regulatory approaches remain a concern; in the United States, for example, 70–75% of critical infrastructure is considered vulnerable to adversarial autonomous systems. To address these issues, we propose a governance model built on three pillars: modular agent design, adaptive safety mechanisms, and international coordination. Policy measures such as licensing thresholds for high-computer systems exceeding 10^25 FLOPs, structured red-team testing across public and private sectors, and fiscal incentives for governance-by-design practices are advanced as actionable pathways. Overall, the study argues for adaptive, globally coordinated governance frameworks that balance innovation with systemic risk mitigation in the era of agentic AI and AGI. his is a pure review paper and all results, proposals and findings are from the cited literature.