Artificial Intelligence in Intent-Based Networking: Enhancing Automation, Scalability, and Reliability

Intent-Based Networking (IBN) has emerged as a promising paradigm in network management, enabling the translation of high-level user intents into automated network configurations. To fully realize the potential of IBN, Artificial Intelligence (AI) plays an important role in enhancing the efficiency, adaptability, and scalability of its core building blocks. This paper explores the role and impact of AI across the main functionalities of IBN, including intent recognition and translation, intent validation and optimization, real-time assurance, and closed-loop feedback systems. We detail how techniques such as Natural Language Processing (NLP), Machine Learning (ML), and Reinforcement Learning (RL) are leveraged to automate and refine each phase of the IBN process. Additionally, we examine the benefits of AI integration with IBN, such as improved network performance, reduced operational complexity, and predictive capabilities, while addressing challenges like interpretability, computational overhead, and data privacy. This study highlights the critical contributions of AI to advancing IBN frameworks and provides a roadmap for future innovations in this field.