AI-Driven Intent-Based Networking Approach for Self-Configuration of Next Generation Networks

Intent-Based Networking (IBN) aims to simplify operating heterogeneous infrastructures by translating highlevel intents into enforceable policies and assuring compliance. However, dependable automation remains difficult because (i) realizing intents from ambiguous natural language into controllerready policies is brittle and prone to conflicts and unintended side effects, and (ii) assurance is often reactive and struggles in multi-intent settings where faults create cascading symptoms and ambiguous telemetry. This paper proposes an end-to-end closedloop IBN pipeline that uses large language models with structured validation for natural language to policy realization and conflictaware activation, and reformulates assurance as proactive multiintent failure prediction with root-cause disambiguation. The expected outcome is operator-trustworthy automation that provides actionable early warnings, interpretable explanations, and measurable lead time for remediation.