Agentic AI For Network Management: Autonomous Troubleshooting And Configuration Through MCP Servers

Abstract

Enterprise networks have grown much more complex, with distributed network topologies, hybrid cloud infrastructure, and on-the-fly configuration. Customary approaches to operationalizing network infrastructure have been reactive and inefficient. Collocating agentic artificial intelligence with the Model Context Protocol allows the development of a scalable system for self-managing networks, where clever agents observe, analyze, plan, execute, and verify via common interfaces. As a replacement for manual operational troubleshooting and configuration processes, the architecture employs automated reasoning cycles for real-time anomaly detection, root cause analysis, and remediation. The Model Context Protocol exposes network functions as clean, typed, and validation-friendly interfaces that allow the use of artificial intelligence agents in the architecture to interoperate with the underlying network infrastructure, enabling a reliable semantic understanding of routing protocols, security policies, interface states, and performance indicators. Pre-execution validation, policy-based verification, rollback, and audit logging are supported as part of the architecture for secure, accountable automated operations. Intent-driven configuration management is especially noteworthy: Natural language requirements are converted into validated network configurations. This reduces time-to-deployment of complex operations across multiple devices and, at the same time, minimizes human error. Stateful processing abstractions, network-wide synthesis techniques, and declarative models enable the diagnosis of problems by separating symptoms from root causes while preserving network-wide invariants across a distributed set of network elements. This enables network healing, compliance enforcement, and operational agility at a scale not achievable by human analysts and administrators. Organizations using agentic network management can achieve orders of magnitude improvement in service availability and operational efficiency, and infrastructure agility, while enabling network and service architects to focus on design rather than implementation. Self-healing networks represent the next generation of enterprise networking infrastructure, where smart automation deals with operational complexity with precision and speed that fundamentally changes the economics and capabilities of network operations.