Agentic AI for Service Assurance in Fixed Broadband Networks: A Conceptual Framework for Intelligent NOC Operations

Abstract

Fixed broadband operators are increasingly challenged by the growing complexity of fiber access networks, heterogeneous home Wi-Fi environments, rising customer expectations, and the need to improve operational efficiency while maintaining service quality. Conventional service assurance approaches largely rely on rule-based monitoring, fragmented operational support systems, and manual intervention by Network Operations Center (NOC) teams. Although Artificial Intelligence (AI) and Machine Learning (ML) have been applied to fault prediction, anomaly detection, and customer experience analytics, these implementations often operate as isolated solutions with limited autonomy and cross-domain coordination.Recent advances in Agentic Artificial Intelligence (Agentic AI) provide an opportunity to transform service assurance through intelligent agents capable of reasoning, planning, knowledge retrieval, and workflow execution. This paper proposes a conceptual framework for integrating Agentic AI into fixed broadband service assurance processes. The framework combines operational data sources, predictive ML models, Large Language Model (LLM)-based intelligence, multi-agent orchestration, and human-in-the-loop governance to support proactive and explainable operational decision-making.The proposed architecture demonstrates how Agentic AI can enhance key assurance functions, including automated root cause analysis, proactive Quality of Experience (QoE) degradation detection, intelligent incident triage, and NOC copilot assistance. Unlike existing studies that focus on standalone AI applications or mobile network scenarios, this work specifically addresses the operational realities of fixed broadband environments. The findings suggest that Agentic AI can reduce Mean Time to Repair (MTTR), improve customer experience, enhance decision consistency, and increase operational efficiency while maintaining governance and regulatory compliance. The study provides both theoretical insights and practical guidance for telecom operators progressing toward intelligent and autonomous service assurance.

Introduction

The text proposes a shift in fixed broadband service assurance from traditional reactive Network Operations Center (NOC) practices toward an AI-driven, autonomous “Agentic AI” framework.

Modern broadband networks (especially FTTH and home Wi-Fi environments) have become highly complex, making manual troubleshooting, alarm-based monitoring, and fragmented OSS tools inefficient. Operators face delayed fault diagnosis, inconsistent decisions, and high operational costs, while customers expect seamless connectivity and fast issue resolution.

Although AI and ML are already used for tasks like fault prediction, anomaly detection, and QoE analysis, these systems are mostly limited to generating insights rather than taking action. Similarly, Large Language Models (LLMs) are used for chatbots, troubleshooting support, and summarization, but they remain advisory tools without real operational autonomy.

To address this gap, the paper proposes an Agentic AI framework that integrates:

• Predictive machine learning for network intelligence
• LLM-based reasoning for contextual understanding
• Multi-agent collaboration for coordinated decision-making
• Workflow execution for operational actions
• Human oversight for governance and control

The study follows a Design Science Research approach and builds a conceptual architecture with five layers: data, ML, LLM intelligence, agent orchestration, and human governance.

Conclusion

This paper proposed a conceptual framework for applying Agentic AI to service assurance in fixed broadband networks. Motivated by the limitations of traditional assurance approaches and the fragmented nature of existing AI applications, the study explored how predictive machine learning, LLM-based reasoning, agent orchestration, and human oversight can be integrated to support intelligent NOC operations. The review of contemporary literature highlighted a notable gap in current research. While AI applications in telecommunications continue to expand, most studies focus on isolated use cases or mobile network environments. Practical frameworks tailored to fixed broadband operations and capable of bridging predictive intelligence with operational execution remain limited. To address this gap, this study introduced a five-layer architecture comprising a Data Layer, Machine Learning Layer, LLM Intelligence Layer, Agent Orchestration Layer, and Human Governance Layer. The framework was further illustrated through practical use cases involving automated root cause analysis, proactive QoE degradation detection, intelligent incident triage, and NOC copilot assistance. The findings suggest that Agentic AI has the potential to improve operational efficiency, enhance decision consistency, reduce Mean Time to Repair (MTTR), and strengthen customer experience outcomes while preserving transparency and human accountability. Rather than replacing operational teams, Agentic AI should be viewed as a collaborative capability that augments human expertise and supports progressive movement toward intelligent assurance. As fixed broadband operators continue their journey toward autonomous operations, Agentic AI represents a promising direction for future service assurance strategies. The framework presented in this paper offers both a theoretical contribution to emerging telecom AI literature and a practical roadmap for operators seeking to modernize assurance practices within increasingly complex broadband environments.

References

This paper proposed a conceptual framework for applying Agentic AI to service assurance in fixed broadband networks. Motivated by the limitations of traditional assurance approaches and the fragmented nature of existing AI applications, the study explored how predictive machine learning, LLM-based reasoning, agent orchestration, and human oversight can be integrated to support intelligent NOC operations. The review of contemporary literature highlighted a notable gap in current research. While AI applications in telecommunications continue to expand, most studies focus on isolated use cases or mobile network environments. Practical frameworks tailored to fixed broadband operations and capable of bridging predictive intelligence with operational execution remain limited. To address this gap, this study introduced a five-layer architecture comprising a Data Layer, Machine Learning Layer, LLM Intelligence Layer, Agent Orchestration Layer, and Human Governance Layer. The framework was further illustrated through practical use cases involving automated root cause analysis, proactive QoE degradation detection, intelligent incident triage, and NOC copilot assistance. The findings suggest that Agentic AI has the potential to improve operational efficiency, enhance decision consistency, reduce Mean Time to Repair (MTTR), and strengthen customer experience outcomes while preserving transparency and human accountability. Rather than replacing operational teams, Agentic AI should be viewed as a collaborative capability that augments human expertise and supports progressive movement toward intelligent assurance. As fixed broadband operators continue their journey toward autonomous operations, Agentic AI represents a promising direction for future service assurance strategies. The framework presented in this paper offers both a theoretical contribution to emerging telecom AI literature and a practical roadmap for operators seeking to modernize assurance practices within increasingly complex broadband environments.

Copyright

Copyright © 2026 Mohammad Mustafa, Mohammed Hussain Moheet, Mohammed Abdul Moheet, Safi Ammar Mohammed, Mohammed Hassan Ali Habeeb. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.