<p>This paper investigates issues of governance in relation to the autonomous AI agents and suggests a conceptual governance approach MLRM. However, in contrast to conventional AI systems, autonomous agents are adaptive, goal-oriented, and independent in their decision-making behaviour, complicating the issues of accountability, safety, and regulation. The study follows the comparative conceptual analysis approach, which compares the available models of governance namely risk-based, principle-based, sector-specific, and audit-based, in five analytical dimensions that include accountability, adaption to the agentic systems, fairness integration, technical embedding and lifecycle oversight. The results are introduced in the form of analytical thoughts instead of empirical confirmation. The study shows that the current structures are partially effective but are not integrated through lifecycle governance and embedded technical controls. The proposed MLRM conceptually manages these gaps with the help of organizing governance at the legal, institutional, technical, and ethical levels. Instead of asserting empirically validated improvements, the research claims that the suggested model may support entirety in governance and successes the risk, especially in dealing with bias, accountability, emergent behaviour, and cross-jurisdictional crises. The article makes a contribution to the literature regarding AI governance by providing a conceptual framework that is organized into lifecycle and could be used to inform future empirical research and policies.</p>