AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks

2026-03-24

AI Summary

This work addresses the challenges of translating natural language intents into network policies, which often leads to errors and conflicts—particularly in multi-intent scenarios—where fault diagnosis is difficult and assurance mechanisms are reactive. To overcome these limitations, the paper proposes an end-to-end closed-loop intent-based networking system that leverages large language models to reliably map high-level intents to executable policies. The system incorporates structured validation and conflict-aware activation mechanisms to ensure policy consistency. Moreover, it enables proactive multi-intent fault prediction and root-cause disambiguation, transforming network assurance from passive response to active, interpretable early warning. Experimental results demonstrate that the approach significantly enhances the trustworthiness of automated operations by delivering actionable early alerts, explainable fault analyses, and quantifiable lead time for remediation.

Technology Category

• Natural Language Processing: Safety and Robustness
• Planning, Routing, and Scheduling: Model-Based Reasoning
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Intent-Based Networking (IBN) aims to simplify operating heterogeneous infrastructures by translating high-level intents into enforceable policies and assuring compliance. However, dependable automation remains difficult because (i) realizing intents from ambiguous natural language into controller-ready 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 closed-loop IBN pipeline that uses large language models with structured validation for natural language to policy realization and conflict-aware activation, and reformulates assurance as proactive multi-intent 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.

Problem

• Intent-Based Networking
• natural language ambiguity
• policy conflict
• multi-intent assurance
• fault root-cause disambiguation

Innovation

• Intent-Based Networking
• Large Language Models
• Closed-loop Automation
• Proactive Assurance
• Root-Cause Disambiguation