Jun 2026· Journal of AI Analytics and Applications· pp. 32-46· 0 citations· 42 references
TL;DR
This bibliometric review characterizes the emerging field through 810 publications retrieved from the Web of Science Core Collection for the period 2023–2025, providing a structured, evidence-based map of agentic AI research to orient researchers and practitioners navigating this rapidly evolving field.
Abstract
The rapid evolution of large language models (LLMs) has catalyzed a shift from passive AI systems toward autonomous agentic architectures capable of reasoning, memory, tool use, and multi-agent collaboration. This bibliometric review characterizes the emerging field through 810 publications retrieved from the Web of Science Core Collection for the period 2023–2025. Annual output rose sharply over this window—from 4 publications in 2023 to 96 in 2024 and 710 in 2025—accompanied by a parallel rise in citations, indicating rapid mainstream adoption. Author-keyword analysis reveals a landscape dominated by large language models, artificial intelligence, and multi-agent systems, with agentic AI, generative AI, and retrieval-augmented generation (RAG) emerging as core themes. Research output is geographically concentrated, led by China and the United States, and is distributed across a broad range of engineering, applied-science, and domain-specific journals rather than a single specialist venue, reflecting the field's cross-disciplinary uptake. Synthesizing this corpus, we organize the technical landscape around reasoning, memory, tool integration and RAG, and multi-agent orchestration; survey application domains spanning healthcare, scientific discovery, education, and software engineering, with emerging activity in finance and law; and analyze the principal challenges—hallucination, trust and robustness, inter-agent coordination, scalability, and governance. The review provides a structured, evidence-based map of agentic AI research to orient researchers and practitioners navigating this rapidly evolving field.
The rapid diffusion of generative artificial intelligence has altered the technological and organizational landscape, shifting scholarly and managerial attention from systems primarily designed to generate content toward increasingly autonomous systems capable of planning, reasoning, coordinating actions, and pursuing goals with limited human intervention. This transition has given rise to the concept of agentic artificial intelligence, a broad category encompassing autonomous agents, multi-agent systems, and emerging forms of AI-enabled organizational actors. Despite growing academic and practitioner interest, the literature remains fragmented across computer science, information systems, management, and organizational studies, with limited conceptual integration regarding the implications of agentic AI for organizational design, governance, and leadership.
This study addresses this fragmentation through a systematic literature review and bibliometric analysis of the emerging research domain of agentic artificial intelligence in organizations. Following PRISMA guidelines, the study employs a structured search strategy using the Scopus and Web of Science databases and applies performance analysis and science-mapping techniques through Bibliometrix and VOSviewer. The analysis identifies the principal intellectual foundations of the field, the most influential authors, journals, and countries, and the thematic trajectories that have shaped scholarly discussions from early research on autonomous agents and multi-agent systems to contemporary debates on AI governance and organizational transformation.
The findings reveal a significant shift from technical investigations of autonomous systems toward questions concerning organizational decision-making, human-agent collaboration, governance mechanisms, and the strategic implications of increasingly autonomous AI systems. Six major research themes emerge from the literature: autonomous decision-making systems, multi-agent collaboration, agentic AI governance, human-agent interaction, organizational transformation and leadership, and ethical and societal risks. Building on these findings, the article develops an Agentic AI Organizational Transformation Framework that conceptualizes agentic AI as a dynamic organizational capability whose outcomes depend on governance arrangements, organizational readiness, and institutional trust.
The study contributes to the literature in three ways. First, it provides the first integrative mapping of agentic artificial intelligence research from an organizational perspective. Second, it advances a conceptual framework linking agentic AI capabilities to organizational outcomes and governance mechanisms. Third, it develops a future research agenda aimed at supporting empirical investigations of autonomous AI systems in complex organizational environments. The article concludes that agentic artificial intelligence should not be viewed merely as an incremental extension of generative AI but rather as a potentially transformative organizational phenomenon that may redefine decision-making processes, leadership practices, and the boundaries between human and artificial agency.
Bogdan Costache, V. Enǎchescu, Costin Petcu· International Journal of Edu...· 0 citations
A unified, taxonomy-driven, and deployment-oriented survey of agentic AI systems, synthesizing recent advances through a modular reference architecture and a four-dimensional taxonomy that characterizes agents along the axes of autonomy, tool use, collaboration, and safety–governance is presented.
Sparsh Bajoria, Shreyanshu Ranjan, Adhitya M et al.· Cognitive Computation· 0 citations
This article examines the emerging paradigm of agentic AI for scientific discovery, traces the conceptual shift from tools to agents, lays out a six-stage workflow spanning literature synthesis to manuscript generation, and reviews practical systems in chemistry, equation discovery, materials science, and general machine learning research.
Alexander Taktakidze· Longevity Horizon· 0 citations
An organizing framework for understanding LLM‐based agents is established, systematically deconstructing both single‐agent and multi‐agent systems into their core components, and the architectural principles and key mechanisms that underpin their intelligence are analyzed.
Yuheng Cheng, Ceyao Zhang, Zheng-Wen Zhang et al.· WIREs Data Mining and Knowle...· 1 citation
This survey bridges the existing gap by presenting a comprehensive blueprint for scientific agents' design and introduces a unified taxonomy based on capability envelope and capability maturity, characterizing both the scope of scientific workflow coverage and the reliability of agent behavior under realistic research conditions.
Xinming Wang, Jian Xu, Sheng Lian et al.· IEEE Transactions on Pattern...· 9 citations
AI Agents are driving the transformation of sci-tech intelligence analysis from "human-in-the-loop" to "human-on-the-loop," enabling intelligent and pipeline-based intelligence production processes.
Hangqi Yang· World Journal of Information...· 0 citations
