Can AI Agents Meet Beyond 5G and 6G Network Requirements?

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.