Beyond Intent Translation: Research Gaps in the Application of Generative AI for Intent-Based Networking
Dániel Ficzere, Gergely Hollósi, Pál Varga · 2025
Intent-Based Networking (IBN) promises to redefine network management by automating operations to align with high-level user intents. The advent of powerful Generative AI (GenAI) models, including Large Language Models (LLMs), could significantly accelerate this transformation. However, cur-rent research remains narrowly focused on LLM-based intent translation, leaving substantial gaps in understanding how GenAI can be applied across the entire IBN life cycle. This paper aims to bridge these gaps by investigating the wider potential of Generative AI (GenAI) in areas like intent orchestration, moni-toring, compliance assessment, and automated actions. Through a systematic categorization of tasks based on GenAI's suitability and the presentation of a practical use case, this work highlights the critical need for more comprehensive research to fully harness the potential of GenAI in advancing IBN.