Reliable LLM-enabled Intent-driven Management: A Validation pipeline for Network Automation

To democratize zero-touch network automation, we need to connect natural language user intents with formal, standards-compliant specifications. Large Language Models (LLMs) are promising but unreliable. We propose a hybrid AI symbolic framework that uses LLMs for translation but en forces correctness with deterministic ”guardrails”. Our Retrieval Augmented Generation (RAG) pipeline generates TM Forum compliant intents and then validates them to ensure syntactic, semantic, and policy compliance. We benchmark our solution using three open-source LLMs, demonstrating the framework’s high reliability. It achieved a 93.8% pipeline success rate with Mistral-7B, where intents were correctly processed to a definitive conclusion. The results suggest that a hybrid architecture with symbolic guardrails offers the safety and reliability necessary for deploying generative AI in network orchestration, overcoming the limitations of using isolated LLMs.