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

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.