Fraunhofer FOKUS' Open6GCore is the first practical implementation of a 6G core network. It challenges the traditional view of network functions as rigidly connected components, by embracing a highly modular software-driven approach that offers significant adaptability and reduced complexity. The flexibility of Open6GCore allows dynamic adaptation to requirements, whether as a 5G core network, simplified microservices, microcore, or a distributed large-scale core network. By implementing 3GPP standards with modern IT methodologies, Open6GCore achieves scalability and resilience and simplifies the addition of experimental features. Thus, it is particularly well-suited for research environments, facilitating rapid prototyping and seamless integration of new functionalities. Initial functional validation demonstrates successful deployment, interoperability with 5G User Equipment and Radio Access Networks, and efficient core network operations. These results demonstrate Open6GCore's readiness as a platform for 5G and 6G testbeds, trials, and innovation. This paper highlights the architecture, development, validation, and integration techniques of Open6GCore, offering insight into its role as a foundational platform for next-generation networks.

As the requirement for flexibility in networks grows, new mechanisms are needed to enable extreme adaptability to diverse environments and the ability to react to unknown situations. The rise of AI provides a significant opportunity to enhance network efficiency, scalability, and resilience beyond what is possible with traditional policy-based approaches. However, current network architectures rely on predefined rules and event-triggered responses, insufficient to dynamically address unpredictable and diverse conditions. This paper introduces a new perspective on AI-driven autonomous network agents, capable of continuous perception, proactive decision-making, and adaptive optimization across multiple network layers. These agents automate deployment-phase tasks, optimize runtime operations, and enhance cross-layer coordination, addressing critical challenges like mobility management, resource scheduling, and dynamic service adaptation. Unlike traditional static policies, these agents learn from operational feedback, enabling networks to adjust and refine their behavior over time. With their self-learning and intent-driven automation, AI-driven agents integrated into future network architectures, including beyond 5G and 6G systems, can enable greater autonomy, adaptive optimization, and improved robustness, effectively responding to evolving communication requirements.