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]

Core Viewpoint - The article discusses the evolving landscape of AI search services, particularly in light of Microsoft's decision to discontinue the Bing Search API, which has created a significant market opportunity for new players like Xiaosu Technology [1][3][22]. Group 1: Market Dynamics - Microsoft's withdrawal from the Bing Search API exposes a substantial market gap, prompting clients who relied on this service to seek alternatives, thus accelerating market share transfer to other service providers [3][4]. - The AI search market is likened to the early days of cloud computing, where large companies focused on consumer-facing services, inadvertently creating opportunities for smaller firms like Xiaosu Technology [8][9]. Group 2: Xiaosu Technology's Strategy - Xiaosu Technology has achieved an annual recurring revenue (ARR) of $25 million within months, indicating strong market traction [2]. - The company emphasizes three core capabilities: global service capacity, alignment with AI agent needs, and comprehensive expansion capabilities, which differentiate it from competitors [9][10][14]. - Xiaosu's intelligent search service covers over half of the leading AI native applications in China, showcasing its market penetration [15][22]. Group 3: Competitive Landscape - The competitive landscape post-Bing's exit includes both overseas and domestic players, with many lacking the necessary language capabilities or comprehensive service offerings to meet market demands [14][16]. - Xiaosu's competitive edge lies in its talent pool, which includes experienced professionals from early search companies, and its robust distributed infrastructure that supports low-latency global services [14][20]. Group 4: Future Outlook - The article suggests that the competition for AI infrastructure is just beginning, with a shift from consumer traffic battles to B2B foundational service contests [22][23]. - As AI evolves, the demand for real-time data and model invocation will become more complex, indicating a growing need for sophisticated search services tailored for AI applications [22].

Core Viewpoint - The article discusses the significant impact of Bing Search API's shutdown on the AI search market, highlighting the emergence of new players like Xiaosu Technology that can fill the gap left by Bing's exit [2][3][4][19][32]. Group 1: Impact of Bing Search API Shutdown - Bing Search API will be completely shut down on August 11, making it difficult for developers to access quality search sources [2][3]. - The shutdown is seen as a strategic move by Microsoft to focus on AI search and integrate it with Azure services, potentially increasing pricing [4][19]. - The AI search market is projected to reach 347.2 billion yuan by 2029, with a growth rate exceeding 20% annually over the next five years [7]. Group 2: Current Market Landscape - The AI search market is characterized by a head effect, with major players focusing on consumer (ToC) services while neglecting business (ToB) opportunities [21][22]. - Traditional search giants like Google and Baidu have strong technical advantages but primarily cater to consumer needs, limiting their API offerings for businesses [22]. - New startups are emerging to provide specialized search services in vertical markets, such as legal and academic fields, to differentiate themselves from established players [23][24]. Group 3: Emergence of New Players - Xiaosu Technology has developed an AI search engine that offers competitive capabilities, achieving an annual recurring revenue (ARR) of $25 million within months [25][32]. - Xiaosu's AI search engine supports over 30 languages and provides features like semantic understanding and full-text display, making it suitable for various applications [29][38]. - The company emphasizes its ability to provide real-time data and multi-modal search capabilities, positioning itself as a strong alternative to Bing [36][38]. Group 4: Future Opportunities - The article suggests that the AI search market is ripe for new entrants, especially in the wake of Bing's exit, with Xiaosu Technology being a notable contender [32][44]. - The demand for AI search capabilities is expected to grow, particularly in the context of developing AI applications and agents [16][43]. - Xiaosu's focus on ToB services and competitive pricing (one-third of Bing's costs) positions it well to capture market share from businesses seeking reliable search solutions [32][45].