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.

翻译：意图网络（IBN）旨在通过将高级意图转化为可执行策略并确保合规性，简化异构基础设施的运维。然而，可靠的自动化仍面临挑战，原因在于：（i）将模糊自然语言中的意图实现为控制器可用的策略既脆弱又易引发冲突和意外副作用；（ii）保障机制常呈被动响应模式，在多意图场景下难以应对故障引发的级联症状与模糊遥测数据。本文提出一种端到端闭环IBN流水线，采用具备结构化验证能力的大型语言模型实现自然语言到策略的转化与冲突感知激活，并将保障机制重新构架为具备根因消歧能力的主动式多意图故障预测。预期成果为运营商可信赖的自动化系统，可提供可操作预警、可解释性说明及可量化的修复缓冲时间。