Journal Press India^®

Author Details ( * ) denotes Corresponding author

Purpose: The paper explores the transformative potential of agentic arti-ficial intelligence (AI) in human resource management (HRM), specifically on the autonomous decision-making aspects and the governance problems that they create within the organization. Instead of offering a qualitative breakaway from traditional AI/ML tools used in HR settings, agentic AI is goal-directed, multi-step reasoning, adaptive planning, and tool orches-tration, implying a qualitative shift.

Design/Methodology/Approach: This paper stands based on systematic conceptual review of the existing literature and combines such theories as Resource-Based View (RBV), Institutional Theory, Socio-Technical Systems (STS) Theory, and AMO Theory to create a new conceptual model. Seven research propositions, which are based on theoretical foundations, are developed and an empirical research design is suggested to be validated in the future.

Findings: The paper proposes four governance imperatives transparency, accountability, bias reduction, and human control, because of which organi-zations are required to mediate the implementation of agentic AI in the key HR functions.

Research Limitations: Conceptual, these propositions and framework have not yet been empirically tested. The use of previously published works could result in limited scope of rapidly emerging agentic AI developments. In theoretical terms, the contextual focus on emerging economies is appro-priate, but it could limit cross-sectoral generalizability to advanced economy situations.

Managerial Implications: HR professionals and HR policy makers should create governance frameworks before using agentic AI in critical HR areas like hiring, performance measurement, and workforce planning.

Originality/Value: The paper contributes in three ways: It proposes actionable governance propositions that are relevant for both practice and policy making.

Keywords

Agentic AI, Human Resource Management, Autonomous Decision-Making, Organizational Governance, and AI Ethics

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