Network slicing, a cornerstone technology for future networks, enables the creation of customized virtual networks on a shared physical infrastructure. This fosters innovation and agility by providing dedicated resources tailored to specific applications. However, current orchestration and management approaches face limitations in handling the complexity of new service demands within multi-administrative domain environments. This paper proposes a future vision for network slicing powered by Large Language Models (LLMs) and multi-agent systems, offering a framework that can be integrated with existing Management and Orchestration (MANO) frameworks. This framework leverages LLMs to translate user intent into technical requirements, map network functions to infrastructure, and manage the entire slice lifecycle, while multi-agent systems facilitate collaboration across different administrative domains. We also discuss the challenges associated with implementing this framework and potential solutions to mitigate them.

Network slicing plays a crucial role in the progression of 5G and beyond, facilitating dedicated logical networks to meet diverse and specific service requirements. The principle of End-to-End (E2E) slice includes not only a service chain of physical or virtual functions for the radio and core of 5G/6G networks but also the full path to the application servers that might be running at some edge computing or at central cloud. Nonetheless, the development and optimization of E2E network slice management systems necessitate a reliable simulation tool for evaluating different aspects at large-scale network topologies such as resource allocation and function placement models.  This paper introduces Slicenet, a mininet-like simulator crafted for E2E network slicing experimentation at the flow level. Slicenet aims at facilitating the investigation of a wide range of slice optimization techniques, delivering measurable, reproducible results without the need for physical resources or complex integration tools. It provides a well-defined process for conducting experiments, which includes the creation and implementation of policies for various components such as edge and central cloud resources, network functions of multiple slices of different characteristics. Furthermore, Slicenet effortlessly produces meaningful visualizations from simulation results, aiding in comprehensive understanding.  Utilizing Slicenet, service providers can derive invaluable insights into resource optimization, capacity planning, Quality of Service (QoS) assessment, cost optimization, performance comparison, risk mitigation, and Service Level Agreement (SLA) compliance, thereby fortifying network resource management and slice orchestration.