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Agentic AI Systems: What It Is and Isn't

Yogesh K. Dwivedi et al.

Global Business and Organizational Excellence2025https://doi.org/10.1002/joe.70018article
ABDC C
Weight
0.52

What the paper says

ABSTRACT The rapid adoption of artificial intelligence (AI) is shifting from tools that assist human tasks toward self‐directed, agentic AI systems capable of planning and executing complex goals with minimal oversight. However, a clear understanding of what distinguishes these systems from conventional AI agents and generative AI is lacking, obscuring their unique opportunities and risks. To this end, this article addresses that gap by defining the core concepts, technologies, and management approaches for agentic AI systems, which utilize planning, shared memory, tools, and multi‐agent teamwork to complete complex tasks autonomously. By contrasting this paradigm with its predecessors, the paper synthesizes recent technical surveys, governance proposals, and early industrial deployments to highlight that while agentic AI enables transformative applications like end‐to‐end process automation and adaptive decision support, it also introduces significant challenges, including cascading errors, goal misalignment, and regulatory gaps. Finally, this paper concludes with strategic guidance for organizations and consumers to adopt the capabilities of these systems responsibly, emphasizing the imperative of maintaining transparency, accountability, and human oversight.

7 citations

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https://doi.org/https://doi.org/10.1002/joe.70018

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@article{yogesh2025,
  title        = {{Agentic AI Systems: What It Is and Isn't}},
  author       = {Yogesh K. Dwivedi et al.},
  journal      = {Global Business and Organizational Excellence},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1002/joe.70018},
}

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Agentic AI Systems: What It Is and Isn't

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Evidence weight

0.52

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.47 × 0.4 = 0.19
M · momentum0.68 × 0.15 = 0.10
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.