Automating Computer Network Management with N8N

Abstract

Manual network operations are increasingly inadequate for managing the scale and complexity of modern infrastructure, leading to severe alert fatigue and high Mean Time to Resolution (MTTR). Existing static automation solutions remain rigid and lack the reasoning capabilities necessary to handle unknown or subtle fault variations. This paper proposes a hybrid multi-agent framework for autonomous network fault remediation, integrating GPT-4o as a cognitive reasoning engine within a six-agent N8N orchestration pipeline over a virtualized FRRouting (FRR)/OSPF testbed. The framework implements a modular 3-tier architecture incorporating the ReAct reasoning paradigm and a Telegram-based Human-in-the-Loop (HITL) safety gate. Evaluated across 30 controlled trials spanning six fault types and four OSI layers, the system achieved a 100% remediation success rate with a mean MTTR of 29.9 seconds — a 67.5% and 83.1% reduction over developer and blind manual baselines respectively. A memory store learning effect was empirically confirmed, demonstrating adaptive behaviour without model retraining, and the HITL bidirectional gate was validated through a deliberate rejection scenario. These results position the framework at TM Forum Level 4 autonomy, establishing LLM-orchestration integration as a viable approach for intelligent, autonomous NetOps.