Intent-Based Networking with Large Language Models: Natural Language Network Policy Specification, Automated Configuration Generation, and Closed-Loop Verification in Enterprise Campus Networks
Intent-Based Networking (IBN) -- which allows network operators to specify desired network behavior in high-level business terms and rely on automation to translate intent into device configurations -- has been a long-standing aspiration of network management research. The emergence of large language models with strong natural language understanding and code generation capabilities has created new opportunities for practical IBN implementation without the manual ontology engineering that constrained earlier approaches. This paper presents NetIntent, an LLM-powered IBN framework that translates natural language network policy statements into vendor-agnostic network configuration artifacts (Ansible playbooks, Cisco IOS-XE, Juniper JunOS) with closed-loop verification against the deployed network state. NetIntent employs a three-stage pipeline: intent parsing and formalization using GPT-4o with network domain-adapted prompting, configuration synthesis using retrieval-augmented generation over a vendor configuration knowledge base, and automated verification using network simulation and configuration diffing. Evaluation is conducted across a 200-device enterprise campus network testbed spanning switching, routing, security policy, and QoS configuration domains. NetIntent correctly translates 87.4 percent of natural language policy statements into syntactically and semantically correct configurations, with the remaining 12.6 percent requiring human clarification due to intent ambiguity. Mean configuration generation time of 23 seconds compares favorably to a baseline of 47 minutes for manual configuration by experienced network engineers. We analyze the failure modes and introduce NetIntent-Guard, a post-generation policy correctness verifier reducing misconfiguration escape rate by 94 percent.
