Agentic artificial intelligence systems represent a paradigm shift from conventional machine learning applications, introducing autonomous, goal-directed agents capable of multi-step planning, persistent state management, and tool-augmented execution. These capabilities create novel governance challenges and risk profiles that extend beyond traditional AI oversight mechanisms. This paper examines the current landscape of agentic AI governance and risk management in enterprise contexts through systematic analysis of recent frameworks, technical architectures, and organizational models. The analysis identifies three primary governance modalities, regulatory, organizational, and technical, and maps emergent risk categories including coordination failures, cascading reliability issues, adversarial threats, and compliance gaps. The paper synthesizes best practices from recent governance frameworks, including runtime enforcement protocols, capability-centric risk mapping, and staged validation approaches. Findings indicate that effective enterprise governance requires layered architectures integrating policy-as-code enforcement, semantic telemetry, dynamic authorization, and auditable provenance mechanisms. The paper concludes with recommendations for governance-by-design principles and identifies critical gaps in standardization, benchmarking, and regulatory adaptation that require further research and cross-sector coordination.

agentic AI, AI governance, enterprise risk management, autonomous systems, compliance frameworks, runtime governance

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