Generative AI and Agentic Systems: Driving Automation and Transforming Operations

  1. Nasif Chowdhury

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Abstract

It is believed that generative AI and agentic are two complementary concepts that are driving the automation and re-engineering of operations in any industry. Generative AI is the best at producing content, whether in the form of text, images, or code, in response to user inputs, whereas agentic systems take this ability and run everything independently and purposefully. This article discusses how the concept of reactively creating content began to change into proactively performing work. It characterizes both paradigms, discusses their technological enablers, such as LLMs, integration models, and orchestration of agents, and explains how agentic AI can be used to automate workflows, make decisions, and reinvent processes. We include case studies in different industries, analyse pros and cons, and write about architectural patterns like the agentic AI mesh item. We conclude with a description of challenging issues and future directions of research.

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  1. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/17254838.

    Does the introduction explain the objective of the research presented in the preprint? Yes
    Are the methods well-suited for this research? Somewhat appropriate
    Are the conclusions supported by the data? Neither supported nor unsupported The paper is conceptual and not data driven
    Are the data presentations, including visualizations, well-suited to represent the data? Neither appropriate and clear nor inappropriate and unclear It focuses more on conceptual framing, case studies, and architectural discussions.
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Very clearly
    Is the preprint likely to advance academic knowledge? Moderately likely Yes, advances knowledge conceptually but not strongly empirical
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

    Use of Artificial Intelligence (AI)

    The author declares that they did not use generative AI to come up with new ideas for their review.

  2. PREreview

    This Zenodo record is a permanently preserved version of a Structured PREreview. You can view the complete PREreview at https://prereview.org/reviews/17168739.

    Does the introduction explain the objective of the research presented in the preprint? Yes
    Are the methods well-suited for this research? Somewhat appropriate
    Are the conclusions supported by the data? Somewhat supported
    Are the data presentations, including visualizations, well-suited to represent the data? Highly appropriate and clear
    How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research? Somewhat clearly
    Is the preprint likely to advance academic knowledge? Somewhat likely
    Would it benefit from language editing? No
    Would you recommend this preprint to others? Yes, but it needs to be improved
    Is it ready for attention from an editor, publisher or broader audience? Yes, after minor changes

    Competing interests

    The author declares that they have no competing interests.

  3. Version published to 10.20944/preprints202509.0690.v1