<![CDATA[The rapid development of artificial intelligence has accelerated the emergence of AI agents, defined as autonomous or semi-autonomous systems that integrate perception, contextual reasoning, decision-making, interaction, and action within defined workflows. Although AI in nursing has been widely reviewed, existing syntheses often combine predictive models, generative tools, decision-support systems, and agent-based architectures, leaving the specific contributions and implementation maturity of AI agents insufficiently differentiated. This focused literature overview examined peer-reviewed publications published between 2021 and 2025 using targeted database searches and a structured narrative synthesis. The review classified the evidence according to agent architecture, automated nursing tasks, implementation maturity, and reported clinical and operational implications. Three overlapping architectural categories were identified: LLM-driven agents, cognitive agents, and multi-agent systems. Applications were concentrated in clinical documentation and handover, predictive monitoring, medication safety, triage, and clinical decision support. The evidence suggests potential improvements in timeliness, documentation consistency, risk detection, workflow integration, and the reduction of repetitive administrative work. The maturity of the evidence varied considerably. Monitoring and sensor-enabled safety systems showed closer links to rsmitheal-world practice, whereas LLM-driven documentation and multi-agent triage systems were more frequently supported by conceptual, prototype, or simulation-based evidence. The synthesis therefore supports supervised task augmentation rather than the replacement of professional nursing judgment. Major implementation requirements include system reliability, bias mitigation, transparent accountability, human oversight, workforce competency, organizational readiness, and clinical governance. Nursing education should prepare practitioners to evaluate AI-generated outputs, recognize uncertainty and inappropriate recommendations, and apply appropriate escalation procedures. Future research should prioritize real-world, multisite, and longitudinal evaluations that measure patient safety, workload redistribution, verification burden, and clinical outcomes. This review provides a focused conceptual distinction between conventional AI decision-support tools and agent-based systems while integrating their clinical, operational, educational, and governance implications.]]>
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