Group 1 - The core viewpoint of the article emphasizes the emergence of AI Agents as foundational components for intelligent operations, moving beyond mere research projects to practical applications in various industries [2][3] - JD Cloud launched JoyAgent-JDGenie, the first complete product-level general multi-agent system, achieving a 75.15% accuracy rate in the GAIA benchmark test, surpassing competitors like OWL and OpenManus [2] - Flowith introduced Neo, the world's first agent supporting "three infinities": infinite steps, infinite context, and infinite tools, enabling complex task execution and extensive memory capabilities [2] Group 2 - The article identifies four core pain points for the implementation of AI Agents: stability and execution chain disruptions, poor data quality and complex integration, decentralized model management, and difficulties in debugging, monitoring, and compliance [4][5][6][7][8] - To address these challenges, a dedicated infrastructure termed "Agent Infra" is proposed, which should provide a robust execution environment, efficient model management, and secure data supply [8][10] - Xiaosu Technology has emerged as a leader in the Agent Infra space, serving nearly a thousand clients globally and covering over half of the top native applications in China [10][11] Group 3 - Xiaosu Technology's infrastructure includes IaaS (AI cloud services), MaaS (model services), and DaaS (data services), which collectively support the operational needs of AI Agents [12][14] - The IaaS layer offers global cloud and computing resources, while the MaaS layer ensures stable model access and management, and the DaaS layer provides high-quality, low-latency data retrieval [12][14] - The integration of these services creates a comprehensive technical foundation for AI Agents, addressing key pain points in perception, collection, reasoning, and feedback [14] Group 4 - The article discusses the necessity for AI Agents to evolve from simple conversational tools to proactive task executors capable of real-time decision-making, highlighting the importance of connected search and real-time data access [15][16] - The Retrieval-Augmented Generation (RAG) process enhances the knowledge retrieval capabilities of Agents, allowing them to provide more accurate and professional responses [19] - The article outlines various enterprise use cases for AI Agents, emphasizing the need for real-time data access to improve customer service, market analysis, financial insights, and developer assistance [21][22] Group 5 - Xiaosu Technology's intelligent search service is positioned as a critical enabler for AI Agents, providing high accuracy, structured retrieval capabilities, and compliance with global regulations [23][25] - The intelligent search supports over 35 languages and various content types, ensuring a comprehensive data service for diverse Agent applications [25][26] - The search service is designed to deliver complete content retrieval, allowing Agents to access full documents and reports in a single call, enhancing efficiency and user experience [27] Group 6 - Xiaosu's intelligent search leverages advanced semantic indexing and multi-stage ranking models to deliver high-quality content tailored to the Agent's query intent [28] - The service guarantees high availability and low latency, with a service level agreement (SLA) of 99.9%, ensuring reliable operation even during peak loads [31] - The article concludes that a stable Agent Infra is essential for the successful deployment of AI Agents, with Xiaosu Technology providing the necessary foundation for their effective operation [33]