Agentic AI Framework for Autonomous Scientific Research Assistance

  • Authors

    • Anatoly Kitov Colonel & Computer Scientist, Soviet Academy of Sciences, Russia. Author
    • Mikhail Kartsev Chief Designer, Research Institute of Computer Systems, Russia. Author

    DOI:

    https://doi.org/10.67228/30715636/IJETMR-2024PII6Q2K

    Published 11-03-2024

  • Agentic Artificial Intelligence, Autonomous Research Systems, Scientific Research Assistance, Large Language Models, Multi-Agent Systems, Retrieval-Augmented Generation, Knowledge Graphs, Scientific Discovery, Explainable Artificial Intelligence, Research Automation

    Issue

    Vol. 7 No. 2 (2024)

    Section

    Articles

    How to Cite

    [1]
    A. Kitov and M. Kartsev, “Agentic AI Framework for Autonomous Scientific Research Assistance”, IJETMR, vol. 7, no. 2, pp. 01–15, Nov. 2024, doi: 10.67228/30715636/IJETMR-2024PII6Q2K.

  • Abstract

    The rapid expansion of scientific publications, experimental data, and digital repositories has made research increasingly data-intensive and computationally complex. Traditional research tools, such as search engines and digital libraries, require substantial manual effort and provide limited support for comprehensive research workflows. This paper proposes an Agentic AI Framework for Autonomous Scientific Research Assistance that integrates Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and collaborative multi-agent systems to automate key research activities. The framework coordinates specialized agents for literature review, knowledge graph construction, hypothesis generation, experiment planning, data analysis, citation management, research validation, and manuscript generation. The architecture combines domain-specific knowledge bases, reinforcement learning-based task optimization, and explainable AI techniques to improve transparency, adaptability, and trustworthiness. It also promotes scientific integrity through citation verification, plagiarism prevention, ethical compliance monitoring, and continuous knowledge updating. Performance is evaluated using literature retrieval accuracy, hypothesis relevance, experiment planning efficiency, collaboration effectiveness, response latency, and manuscript quality. Experimental results indicate that coordinated autonomous agents significantly reduce research time, improve workflow consistency, enhance knowledge discovery, and increase scientific productivity compared with conventional AI-based research assistants. The modular framework supports scalable deployment across cloud, edge, and hybrid environments while enabling interdisciplinary collaboration, providing a reliable foundation for trustworthy AI-assisted scientific research.

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