Building an Agentic AI Network Debugger

Authors: Sujay Kanungo

DOI: https://doi.org/10.37082/IJIRMPS.v13.i6.232860

Short DOI: https://doi.org/hbf8zj

Country: United States

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Abstract: In an increasingly complex digital landscape, the need for robust debugging tools that leverage artificial intelligence (AI) has never been more pressing. This paper presents the concept of an Agentic AI Network Debugger, designed to autonomously identify, diagnose, and resolve issues within network systems. We explore the integration of advanced AI techniques, including machine learning and natural language processing, to enhance the debugging process, making it more efficient and user-friendly. The proposed framework employs an agent-based architecture, allowing for real-time monitoring and analysis of network behaviors, while also adapting to emerging anomalies through continuous learning. By synthesizing theoretical insights and practical applications, this work contributes to the ongoing discourse on intelligent debugging solutions, ultimately aiming to facilitate smoother network operations and reduce downtime. The paper concludes with a discussion of future research directions and the potential impact of agentic AI in the field of network management.

Keywords: Agentic AI, Networking, Artificial Intelligence.

Paper Id: 232860

Published On: 2025-11-20

Published In: Volume 13, Issue 6, November-December 2025

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