Responsible Governance of Agentic Artificial Intelligence within Human Resource Management Systems: A Lifecycle Framework for Accountability, Oversight, and Risk Control
Keywords:
AI, agentic, AI governance, Human Resource Management, AI risk managementAbstract
Agents based on artificial intelligence (AI) are increasingly embedded within enterprise human resource management systems (HRMS) to automate recruitment, screening, performance, learning, and workforce analytics. Unlike static decision-support tools, agentic AI systems pursue goals, invoke tools, and execute multi-step workflows with limited human supervision, raising governance challenges that existing principles-based frameworks do not fully resolve. This article develops a conceptual governance framework for responsible agentic AI within HRMS. A structured review of peer-reviewed literature and authoritative standards, including the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, and the OECD AI Principles, identifies the governance gap: existing instruments articulate principles and risk-management functions but are domain-agnostic and do not operationalize controls for agent autonomy, lifecycle dynamics, or HR-specific risks. The proposed Responsible Agentic AI Governance Framework (RAGF) integrates five lifecycle phases, design and authorization, deployment and provisioning, operation and monitoring, audit and assurance, and review and retirement, with six governance control layers and a four-tier risk-control model that maps risk levels to autonomy modes and concrete controls. The framework is analytically evaluated against existing approaches and HR governance requirements. The contribution is an integrated, operational governance architecture that translates abstract responsible-AI principles into lifecycle-oriented controls for agentic AI in HR systems, addressing accountability, transparency, human oversight, auditability, and risk mitigation.
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