Application research on intelligent probing based on large-scale AI models in internet fault handling

Hongli Gao; Xielina Abduréheman; Lingjuan Che; Mierxiati Maimaiti Rouzi; Peng Li; Mierkaiti Lazike; Xiao Lyu; Xin Zhao; Chuanjiang Zhang; Jiahui Chen

doi:10.54254/2977-3903/2026.32426

Hongli Gao^1,* , Xielina Abduréheman¹ , Lingjuan Che¹ , Mierxiati Maimaiti Rouzi¹ , Peng Li¹ , Mierkaiti Lazike¹ , Xiao Lyu¹ , Xin Zhao² , Chuanjiang Zhang³ , Jiahui Chen⁴

¹ China Mobile Communications Group Xinjiang Co., Ltd.

² Ningbo Putian Communication Technology Co., Ltd.

³ Feisida Technology (Beijing) Co., Ltd.

⁴ Chengdu Ruinuode Technology Co., Ltd.

^* Corresponding author: gaohongli@xj.chinamobile.com

Published: 26 March 2026

DOI: https://doi.org/10.54254/2977-3903/2026.32426

Abstract

With the exponential growth in the complexity of Internet architectures and the widespread adoption of cloud-native service technologies, traditional operation and maintenance (AIOps) models—largely reliant on the paradigm of "expert rules + fixed scripts"—have become increasingly passive and inefficient when confronted with unknown faults and massive volumes of alerts. This study focuses on the application of large-scale AI model–based intelligent agents across the full lifecycle of Internet fault handling, aiming to construct autonomous O&M agents endowed with capabilities of perception, decision-making, and execution. The paper first analyzes the core challenges in current fault management: alert storms leading to missed and false incident reports, cross-system data silos hindering root cause localization, and heavy reliance on expert experience in manual troubleshooting, resulting in delayed response times. On this basis, a hierarchical solution architecture based on large-model agents is proposed, comprising a multi-source data perception layer, a fault reasoning and decision-making layer, and an automated execution layer [1]. By integrating retrieval-augmented generation (RAG) techniques with an O&M knowledge base, the proposed approach equips intelligent agents with the ability to interpret topology metrics, log semantics, and change events. Furthermore, the introduction of chain-of-thought reasoning and reflection mechanisms enables the agents to simulate expert diagnostic pathways, thereby achieving millisecond-level anomaly detection and minute-level root cause identification.

Keywords: large-scale AI models , intelligent agents , Internet fault handling , root cause analysis , self-healing operations , AIOps

AEI Vol.17, Issue 4

ISSN (Print): 2977-3903

ISSN (Online): 2977-3911

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Received: 24 February 2026

Published: 26 March 2026

DOI: https://doi.org/10.54254/2977-3903/2026.32426

Article Type: Research Article

Cite this article

Gao,H.;Abduréheman,X.;Che,L.;Rouzi,M.M.;Li,P.;Lazike,M.;Lyu,X.;Zhao,X.;Zhang,C.;Chen,J. (2026). Application research on intelligent probing based on large-scale AI models in internet fault handling. Advances in Engineering Innovation,17(4),59-68.

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Journal: Advances in Engineering Innovation

Volume: Vol.17, Issue 4

ISSN: 2977-3903 (Print) / 2977-3911 (Online)

Article Information

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