Intent-Based Networking (IBN) enables operators to specify high-level outcomes while the system translates these intents into concrete policies and configurations. As IBN deployments grow in scale, heterogeneity and dynamicity, ensuring continuous alignment between network behavior and user objectives becomes both essential and increasingly difficult. This paper provides a technical survey of assurance and conflict detection techniques in IBN, with the goal of improving reliability, robustness, and policy compliance. We first position our survey with respect to existing work. We then review current assurance mechanisms, including the use of AI, machine learning, and real-time monitoring for validating intent fulfillment. We also examine conflict detection methods across the intent lifecycle, from capture to implementation. In addition, we outline relevant standardization efforts and open-source tools that support IBN adoption. Finally, we discuss key challenges, such as AI/ML integration, generalization, and scalability, and present a roadmap for future research aimed at strengthening robustness of IBN frameworks.

The field of Intent Based Networking (IBN) has recently focused on the use of systems powered by Generative Artificial Intelligence and Large Language Models (LLMs) to generate and configure the network. While being a powerful tool to assist Network Administrators, these methods are far from perfect. They tend to hallucinate and generate bad or sub-optimal configurations. To avoid these problems, we propose the integration of Test-Driven development to the world of Softwarized Networks, as a feedback for automated assistants such as LLMbased configuration generator, leveraging technologies such as Atomic Predicates (AP) and Retrieval Augmented Generation (RAG). In this paper, we describe the usage of an assistant over a softwarized network, making it work in conjunction with Network Policy Enforcement, generating an effective Test-Driven Development for Softwarized Network. Our findings highlight the potential of these combined approaches, mutually benefit each other to reach the goal of a powerful co-pilot for Complex Network Configuration.

Intent-Based Networking (IBN) has simplified network management and orchestration at a high level, but configuring User Equipment (UE), like 5G modems, is still a complex and demanding task due to dynamic requirements and intricate device-specific settings, and scalability challenges, especially when dealing with distributed, edge-based devices. This paper explores the potential of using Intelligent Chatbots powered by Generative Artificial Intelligence and Large Language Models (LLMs) operating as co-pilots to automate and optimize modem configurations. We propose a scalable chatbot system that translates user intents into actionable configurations, enhancing security, performance, and adaptability. To this end, we introduce a middleware that bridges LLMs with 5G Modem interfaces, eliminating retraining needs while ensuring engaging, real-time interaction with users. Additionally, we analyze key challenges in integrating LLM-based chatbots with UE and discuss the benefits of query caching in optimizing response times. Our findings highlight the potential of Intelligent Chatbots in extending IBN principles to UE, enabling a more automated and user-friendly approach to network configuration.

Intent-based Networking (IBN) has emerged as an innovative approach to automate the provisioning of network services while simplifying the interaction between the users and the network, allowing users (e.g., administrators) to define high-level desired outcomes (i.e., intents), and translating expressed intents into automated network configurations. One of the main challenge in IBN is the correct acquisition of the user intents and subsequently the accurate translation into actionable configurations to enforce into the network. Despite some efforts in improving user-to-IBN system interaction, a gap still remains in ensuring satisfactory user experiences and contextually appropriate and coherent responses or translation results. To this purpose we consider using recent advancements in Generative AI, and in particular in Large Language Models, a promising approach to enhance IBN in the scope of intent acquisition and translation. Accordingly, this work investigates the integration of IBN systems with LLM-based Conversational Agents (i.e., intelligent chatbots), on the one hand to enhance the user experience while injecting intents and, on the other hand, to assure an accurate understanding of user intents and their translation into a coherent set of network configurations, which are generated automatically. The chatbot operation according to the proposed approach is illustrated in a DHCP configuration use case.

Intent-based networking has attracted interest in the academic research for enhancing network management operations with user-oriented features. One of the main challenge in this field is the acquisition of the user intents and subsequently the relative translation into policies for the automatic management of the network. Concerning this task, the primary technique employed is relying on Graphical User Interfaces (GUI)s. In addition, the use of Natural Language Processing techniques has been extensively adopted for improved user experience. Recently, some preliminary studies have shown that using Large Language Models (LLMs) for this purpose leads to achieve interesting results. However, based on a comprehensive analysis of the state of the art, it has emerged that the works utilizing the LLMs do not fully exploit all the capabilities these tools could potentially offer. For this reason, the doctoral work aims to address the following challenges: enhancing user experience through the utilization of intelligent chatbots, improving the correct understanding of user intents and ensuring the translation of user intentions into a coherent set of network configurations, which are generated automatically.