Agentic AI Framework for Autonomous Workforce Analytics and Decision Support in Enterprise HRIS Systems

Abstract

The fast evolution of artificial intelligence has introduced a new paradigm for enterprise human resource management, in which agentic AI systems can autonomously manage complex workforce analytics and decision-support functions embedded in Human Resource Information Systems (HRIS). This paper presents a novel Agentic AI Framework that combines multi-agent architectures, large language models (LLMs), and real-time data pipelines to empower autonomous reasoning, planning, and execution of key HR functions such as talent acquisition, workforce planning, performance evaluation, attrition prediction, and compliance monitoring. In contrast to conventional rule-based systems or predictive analytics systems, the proposed framework uses goal-directed agents that can (a) dynamically decompose complex tasks, (b) retrieve contextual enterprise data, and (c) generate actionable recommendations when decision context is uncertain while preserving human oversight for high-impact decisions. It collects information through retrieval-augmented generation (RAG), utilizes tools, and feeds what it learns back into future interactions so organizational decisions remain aligned with enterprise goals. Experimental evaluations on a simulated enterprise-level HRIS environment show considerable improvements in decision-making accuracy, operational efficiency, and HR process automation compared with baseline systems. By systematically embedding ethical considerations around bias mitigation, transparency, and human-in-the-loop oversight, this work provides an actionable, scalable, interpretable, and enterprise-ready foundation for the deployment of agentic AI in modern workforce management ecosystems.