Agentic AI: Vision and challenges

Sukhpal Singh Gill, Subramaniam Subramanian Murugesan, Kumar Ankur Anurag, Prabal Verma, Harkiran Kaur, Surendra Kumar and Mohit Kumar

PLOS Complex Systems, 2026, vol. 3, issue 8, 1-11

Abstract: Agentic AI systems are increasingly viewed as a viable response to the shortcomings of static, rigid, and human-in-the-loop Artificial Intelligence (AI) systems. This is because autonomous operation enables rapid adaptation to dynamic, complex problems with improved time-critical behaviour under real-world constraints. Despite significant progress, current agentic pipelines are still challenged by output instability, scalability gaps, and system integration issues. Addressing these limitations, this article presents a comprehensive conceptual framework unifying core AI functionality with implementation approaches across different system scales, including Agentic AI builds upon Large Language Models (LLMs). Furthermore, the popular applications of Agentic AI and areas for future investigation and open problems are systematically presented.

Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/complexsystems/article?id=10.1371/journal.pcsy.0000120 (text/html)
https://journals.plos.org/complexsystems/article/f ... 00120&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcsy00:0000120

DOI: 10.1371/journal.pcsy.0000120

Access Statistics for this article

More articles in PLOS Complex Systems from Public Library of Science
Bibliographic data for series maintained by complexsystem ().