Traditional AI systems depend on continuous human supervision and find it hard to plan, decide and complete multi-step goals on their own. Agentic AI addresses this by building systems that can perceive their environment, reason, plan and act autonomously. This paper presents a review of five recent studies on agentic AI, covering quantum-enhanced decision intelligence, quantum agents, hybrid multi-agent systems in smart manufacturing and agentic AI in education. The paper first explains the basic concepts, components and design patterns of agentic systems. It then summarises and compares the selected studies, discusses applications, evaluation and ethical issues, and identifies research gaps and challenges. The review shows that agentic AI is a promising direction for autonomous planning and decision-making in complex systems, but issues of reliability, safety, evaluation, cost and real-world deployment remain open.
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Jagtap et al. (2026) studied this question.
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