Agentic AI automation framework for computer network monitoring and management

Hishamuddin, Alif Najwan and Habaebi, Mohamed Hadi and Islam, Md. Rafiqul and Al-Hussaini, Mohammed Abdulla Salim (2026) Agentic AI automation framework for computer network monitoring and management. network, 6 (4). pp. 1-35. ISSN 2673-8732

PDF - Published Version Restricted to Registered users only Download (2MB) | Request a copy

Abstract

Autonomousnetwork managementrequires balancing rapid incident resolution with strict operational safety. This paper introduces a production-inspired, end-to-end multi-agent orchestration architecture for network fault diagnosis and resolution. Built upon an eventdriven N8Norchestrator and integrated with live routing daemons, the framework couples the iterative Reasoning and Acting (ReAct) paradigm with topological Retrieval-Augmented Generation (RAG).Topreventunintendedstatedisruptions, proposedremediationcommands are evaluated using a dynamic Command Risk Score (RC) and governed by a state-aware, two-way interactive Telegram Human-in-the-Loop (HITL) mechanism. Within the scope of six simulated fault scenarios evaluated on a three-router testbed, the multi-agent framework achieved high diagnostic precision and successful network restoration, significantly mitigating diagnostic hallucinations and reducing required human intervention. Furthermore, the system reduced Mean Time to Resolution (MTTR) compared to human engineering baselines. Furthermore, memory decay-based retrieval yields a 32.6% reduction in token consumption and a34.4%operational cost saving per remediation event. This architecture demonstrates strong feasibility as a controlled proof-of-concept for automated diagnostic and remediation workflows within localized network environments.

Item Type:	Article (Journal)
Uncontrolled Keywords:	autonomous network management; closed-loop fault remediation; multi-agent LLMsystems; ReAct reasoning architecture; Partially Observable Markov Decision Process (POMDP); Human-in-the-Loop (HITL) risk scoring; Retrieval-Augmented Generation (RAG); network telemetry & diagnostic accuracy
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	19 Oct 2026 09:14
Last Update:	19 Oct 2026 09:14
Queue Number:	2026-10-Q5656
URI:	http://irep.iium.edu.my/id/eprint/131997
Indexed In:	Google Scholar

Actions (login required)

View Item