Autonomous networks have become a critical focus for the digital transformation of global telecom operators. Currently, these networks are transitioning from L3 conditional autonomy to L4 high-level autonomy, with a key emphasis on enhancing network analysis, decision-making, and intent-driven experience capabilities. In network operations and management, the complexity and volume of data present significant challenges. Basic AI technologies are insufficient to handle this data directly. Instead, intelligent agents, powered by AI, act as the "brain" of the system, leveraging advanced technologies such as data perception, knowledge bases, toolchains, and task planning to manage more sophisticated processes. These intelligent agents are essential for the evolution from L3 to L4 autonomous networks. Key use cases and expected outcomes include: 1. Fault Handling Agent: This agent can monitor the entire network in real-time, analyze vast amounts of data, and rapidly detect and resolve faults. This significantly enhances the efficiency and effectiveness of fault management. 2. This agent dynamically assesses the network's current conditions and user demands, enabling intelligent optimization of wireless networks. It reduces the need for manual intervention while improving the quality and efficiency of network optimizations.