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Traditional AI applications have focused more on model training and inference, while AI Agents must also handle complex processes such as task decomposition, context management, tool calls, knowledge retrieval, and multi-agent collaboration. This means future AI computing environments will require not only large-scale parallel computing capabilities, but also stronger general-purpose computing, high-concurrency processing, and more flexible resource scheduling. In particular, when multiple Agents run simultaneously, many tasks involve data preprocessing, business logic decisions, network communications, and system scheduling. The efficiency of collaboration between CPUs and AI accelerators will directly affect overall application performance. As a result, CPUs with high core counts, strong performance, scalability, and robust virtualization capabilities are becoming a key foundation for next-generation AI computing platforms.