Mapping the Research Landscape of Agentic Artificial Intelligence: A Bibliometric Analysis of Scopus Publications

Context: Agentic Artificial Intelligence (Agentic AI) is rapidly emerging as a major research paradigm in which artificial intelligence systems move beyond response generation toward autonomous, goal-directed reasoning, planning, tool use, memory, adaptation, and multi-agent collaboration. The rapid expansion and disciplinary fragmentation of this literature create a need for systematic mapping of its research landscape.

Aim: This study maps the global development, scholarly influence, geographical distribution, and collaboration patterns of Agentic AI research during 2023–2026.

Approach: A bibliometric and science-mapping analysis was conducted using Scopus publications retrieved on 1 July 2026. Following systematic screening and duplicate removal, 3,425 publications were analyzed using Biblioshiny.

Key Findings: Agentic AI exhibited an annual growth rate of 215.04%, with 9,166 authors contributing across 1,015 sources. Conference papers dominated the corpus (2,654), reflecting the field’s rapidly evolving technological character. China and the United States were the leading corresponding-author countries, while Tsinghua University was the most productive affiliation. Citation analysis identified several highly influential foundational studies.

Interpretation: The findings indicate that Agentic AI is developing rapidly into a distinct, collaborative, and increasingly established research domain centered on autonomous and coordinated AI systems.

Contribution: This study provides a broad Scopus-based quantitative map of contemporary Agentic AI scholarship and identifies emerging priorities for evaluation, multi-agent coordination, security, explainability, human–AI collaboration, and governance.