AI-Augmented Bibliometric Framework: A Paradigm Shift with Agentic AI for Dynamic, Snippet-Based Research Analysis

Our paper introduces a generative, multiagent AI framework designed to overcome the rigidity, limited flexibility and technical barriers of current bibliometric tools. The objective is to enable researchers to perform fully dynamic, code-based scientometric analysis using natural language NL instructions, eliminating the need for specialized programming skills while expanding analytical depth. Methodologically, the system integrates four coordinated AI agents: a custom analytics generator, a full-paper retriever, including a Retrieval Augmented Generation RAG based researcher assistant and an automated report generator. User queries are translated into executable Python scripts, run within a sandbox ensuring safety, reproducibility and auditability. The framework supports automated data cleaning, construction of co-authorship and citation networks, temporal analyses, topic modeling, embedding based clustering and synthesis of research gaps. Each analytical session produces an exportable, end to end report. The novelty lies in unifying NL to code scientometrics, multimodal full paper retrieval, agentic exploration and dynamic metric creation in a single adaptive environment, capabilities absent in existing platforms: VOSviewer, Bibliometrix, SciMAT. Unlike static GUI based workflows, the proposed framework supports iterative what if analysis, hybrid indicators and user driven pipeline modification. Results demonstrate that the framework generates valid analysis scripts, retrieves and synthesizes full papers, identifies frontier themes and produces reproducible scientometric outputs. It establishes a new paradigm for accessible, interactive and extensible bibliometric knowledge.