# NetIntent: Leveraging Large Language Models for End-to-End Intent-Based SDN Automation

arXiv: https://arxiv.org/abs/2507.14398 · HTML: https://mlsanity.com/paper/2507.14398/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2025.

## Built on by (most cited first, 6 of 6)

- [LEAD-Drift: Real-time and Explainable Intent Drift Detection by Learning a Data-Driven Risk Score](https://mlsanity.com/paper/2602.13672/) (2026)
- [MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking](https://mlsanity.com/paper/2602.14283/) (2026)
- [AI-driven Intent-Based Networking Approach for Self-configuration of Next Generation Networks](https://mlsanity.com/paper/2603.23772/) (2026)
- [Intent2Tc: Automated Intent-to-Traffic Control Translation with Language Models](https://mlsanity.com/paper/2609.31397/) (2026)
- [Untangling Co-Drift: Proactive Multi-Intent Failure Prediction and Root-Cause Disambiguation for Self-Driving Networks](https://mlsanity.com/paper/2607.25989/) (2026)
- [Benchmarking LLM-Guided Control-Plane Policies for Backend Fault Isolation in HAProxy](https://mlsanity.com/paper/2608.10532/) (2026)

## Abstract (arXiv)

Intent-Based Networking (IBN) often leverages the programmability of Software-Defined Networking (SDN) to simplify network management. However, significant challenges remain in automating the entire pipeline, from user-specified high-level intents to device-specific low-level configurations. Existing solutions often rely on rigid, rule-based translators and fixed APIs, limiting extensibility and adaptability. By contrast, recent advances in large language models (LLMs) offer a promising pathway that leverages natural language understanding and flexible reasoning. However, it is unclear to what extent LLMs can perform IBN tasks. To address this, we introduce IBNBench, a first-of-its-kind benchmarking suite comprising four novel datasets: Intent2Flow-ODL, Intent2Flow-ONOS, FlowConflict-ODL, and FlowConflict-ONOS. These datasets are specifically designed for evaluating LLMs performance in intent translation and conflict detection tasks within the industry-grade SDN controllers ODL and ONOS. Our results provide the first comprehensive comparison of 33 open-source LLMs on IBNBench and related datasets, revealing a wide range of performance outcomes. However, while these results demonstrate the potential of LLMs for isolated IBN tasks, integrating LLMs into a fully autonomous IBN pipeline remains unexplored. Thus, our second contribution is NetIntent, a unified and adaptable framework that leverages LLMs to automate the full IBN lifecycle, including translation, activation, and assurance within SDN systems. NetIntent orchestrates both LLM and non-LLM agents, supporting dynamic re-prompting and contextual feedback to robustly execute user-defined intents with minimal human intervention. Our implementation of NetIntent across both ODL and ONOS SDN controllers achieves a consistent and adaptive end-to-end IBN realization.