Abstract: Organizations are racing to deploy agentic AI systems across human resources functions, driven by vendor hype and fear of competitive disadvantage. However, most HR use cases labeled "agentic" are actually deterministic workflows with inflated costs and unnecessary complexity. This article examines the critical distinctions between AI tasks, workflows, and autonomous agents in HR contexts, drawing on implementation evidence and practitioner experience to establish decision frameworks for technology selection. Research on algorithmic management, procedural justice, and system trust reveals that autonomous agent deployment often creates more problems than it solves—particularly around cost control, auditability, bias detection, and stakeholder acceptance. Through analysis of real-world HR implementations and recent guidance from AI system architects, we present four diagnostic questions that help practitioners determine when workflows outperform agents: task complexity, economic justification, AI capability alignment, and error tolerance. The evidence suggests that well-governed, human-supervised workflows deliver superior outcomes for approximately 80–90% of current HR AI applications, reserving true agentic systems for genuinely complex, high-value scenarios where dynamic decision-making justifies increased cost and reduced control.
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