Agentic AI Powered Talent Analytics Enabling Talent Discovery: A Systematic Literature Review

Agentic artificial intelligence (AI) is redefining talent analytics by shifting from static, backward-looking dashboards to autonomous systems that sense, decide, and act across the talent lifecycle. Agentic AI agents can continuously ingest multi-source data from applicant tracking systems, HRIS platforms, professional networks, learning systems, and external labor market feeds to construct rich, skills-based profiles of both internal and external talent. Through advanced natural language processing, semantic search, and graph-based skills intelligence, these agents move beyond rigid keyword matching to uncover adjacent and transferable skills, thereby revealing hidden or non-obvious candidates and expanding the effective talent pool. Predictive models embedded within agentic workflows estimate role fit, performance potential, attrition risk, and mobility options, enabling more precise shortlisting and proactive workforce planning. In parallel, conversational and orchestration capabilities allow agents to autonomously execute tasks such as personalized outreach, interview scheduling, status updates, and pipeline nurturing, creating closed-loop “discover–decide–act–learn” cycles. This combination of analytics and autonomy positions agentic AI as a powerful enabler of data-driven, always-on talent discovery that can materially reduce time-to-hire, elevate decision consistency, and improve alignment between evolving skill demands and available talent. However, the same mechanisms introduce heightened risks around algorithmic bias, opacity, regulatory compliance, and over-automation of judgment-laden decisions. As organizations experiment with agentic talent analytics, robust governance, transparent model design, and human-in-the-loop controls become essential to ensure that autonomous agents augment, rather than displace, expert HR decision-making. Taken together, the emerging literature suggests that agentic AI powered talent analytics can transform talent discovery into a strategic, continuous, and skills-centric capability, provided that technical innovation is matched by ethical, organizational, and regulatory discipline.