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The Troubleshooter That Never Sleeps: Continually Learning Agentic AI for Fault Debugging in IEC Continuum

Lookup NU author(s): Ruitao Xue, Dr Rui SunORCiD, Dr Sultan Altarrazi, Dr Dev JhaORCiD, Yinhao Li, Paul Wealls, Dr Tomasz Szydlo, Professor Raj Ranjan

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Abstract

The growing ubiquity of cyberphysical systems (CPSs) embedded in the Internetof Things (IoT)–edge–cloud (IEC) continuum is transforming how data-drivenapplications are deployed and operated. Existing approaches to fault detection,diagnosis, and healing in such CPS deployments predominantly rely on centralizedor statically supervised machine learning models. Due to the growing complexityof CPS systems, such as autonomous vehicles and smart cities, which requiretime-sensitive responses and utilize resource-constrained IoT and edge devices,fault detection presents several formidable research challenges. In this regard,multiagentic artificial intelligence, coupled with lifelong learning, offers a promisingfoundation. Despite its promise, realizing such decentralized and intelligent faultmanagement paradigms becomes not just beneficial, but necessary.

Publication metadata

Author(s): Xue R, Sun R, Altarrazi S, Jha DN, Li Y, Wealls P, Szydlo T, Dustdar S, Ranjan R

Publication type: Article

Publication status: Published

Journal: IEEE Internet Computing

Year: 2026

Volume: 30

Issue: 3

Pages: 81-88

Online publication date: 15/07/2026

Acceptance date: 02/04/2018

Date deposited: 03/08/2026

ISSN (print): 1089-7801

ISSN (electronic): 1941-0131

Publisher: IEEE

URL: https://doi.org/10.1109/MIC.2026.3662039

DOI: 10.1109/MIC.2026.3662039

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