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Traditional AI applications have focused primarily on model training and inference, while AI Agents need to handle complex workflows involving task decomposition, context management, tool invocation, knowledge retrieval, and multi-agent collaboration. This means future AI computing environments will need 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 coordination 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.