2025年被视为智能体元年。在经历此前百模大战的喧嚣后，产业界正逐步达成共识——大模型若无法嵌 入具体场景解决复杂任务，将难以产生商业闭环。而智能体，成为大模型落地的"最后一公里"，必要性 日益凸显。 "在当前阶段，虽然重量级AI产品不断发布，但用户仍然面临终端孤岛、应用孤岛的痛点。"在近日广州 举行的2025数智科技生态大会上，中电信人工智能科技有限公司副总经理杨戈更为直接地点出了当前 AI发展的这一痛点。 针对近期引发热议的AI手机，杨戈指出，AI虽然改变了交互模式，但尚未彻底改变"用户找服务"的路 径。用户为了完成一个跨场景任务（如规划旅行），仍需在多个APP间跳转。 正是在此背景下，中国电信于此次大会上正式发布了星辰智能体服务平台1.0。据杨戈介绍，该平台打 造的跨终端统一智能体入口——"星小辰"，通过该入口，用户无须跳转多个APP，即可通过对话完成复 杂任务，实现"开口即办、智能应需而来"。 四大能力支撑"中控大脑" 下，运营商凭借海量数据、广泛连接和全场景覆盖优势，正成为智能体产业的核心玩家。 而支撑该平台"对话即服务"体验的则是四大核心能力体系。其中，模型能力是智能体的大脑。据杨戈介 绍，星辰平 ...

星辰智能体服务平台1.0之所以能实现"对话即服务"的智能体验，关键在于其构建了四大核心能力体 系。这些能力相互协同，共同塑造了全新的智能交互入口，让服务更加精准、高效、安全。 中电信人工智能公司积极践行"云改数转智惠"战略，以AI技术重构基础业务，打造电信统一的AI入 口"星小辰"智能体，让AI惠及更多用户和合作伙伴，切实推动AI普惠。星辰智能体服务平台1.0具有更 兼容、更简单、更开放的三大特点。 本报讯 （记者李乔宇）12月5日，2025数智科技生态大会期间，中国电信星辰智能体服务平台1.0（以下 简称："星辰智能体服务平台1.0"）正式发布，以"智能体中控引擎与多场景智能体协同架构"为核心， 以AI重构基础业务，构建覆盖CHB全业务的智能服务生态。平台打造"星小辰"智能体作为跨终端的统一 智能体入口，通过意图中控与任务分发引擎，实现对话即服务的体验，让用户无需跳转多个APP即可通 过对话完成复杂任务，真正实现"开口即办、智能应需而来"，为用户提供更加智能便捷的服务体验。 该平台的发布标志着中国电信集团有限公司（以下简称"中国电信"）在人工智能领域的深度布局与全新 突破，致力于打造中国电信统一AI入口，全 ...

Core Viewpoint - The article emphasizes the importance of high-quality datasets in developing and training AI models, highlighting that such datasets are crucial for enhancing model performance and accuracy [4][6][14]. Group 1: High-Quality Data Sets - The company has amassed over 10 trillion tokens of general model corpus data and specialized datasets covering 14 key industries, with a total storage capacity of 350TB [1][6]. - These datasets are not just raw data but are meticulously labeled and optimized, making them ready for immediate application in various industries [3][4]. - High-quality datasets are essential as they directly influence the accuracy, generalization, and usability of AI models, serving as the foundation for effective model training [4][5]. Group 2: Technological Infrastructure - The company has developed the Xingchen MaaS platform, which operates as a data refinery, creating a complete closed loop of "data-model-service" [6][17]. - The platform includes a data toolchain that efficiently processes various data types and a model toolchain that enhances data into usable models, ensuring a robust data lifecycle management [18][19]. - The platform's capabilities allow for the generation of synthetic data for rare or extreme scenarios, enhancing model robustness and safety [18][19]. Group 3: Strategic Considerations - The company's investment in high-quality datasets is driven by national strategy, market demand, and its own operational advantages, positioning itself as a key player in the AI landscape [15][16]. - The government has recognized AI as a national strategy, prompting the company to build data infrastructure that supports AI technology breakthroughs [15][16]. - The company aims to leverage its extensive data resources and customer base to enhance its capabilities in high-quality dataset development [16]. Group 4: Industry Applications - The company has successfully implemented AI solutions in various sectors, such as textile quality inspection, achieving over 95% accuracy in defect detection, significantly improving production efficiency [9][26]. - High-quality datasets have been developed for multiple industries, including healthcare, agriculture, and smart cities, demonstrating the versatility and impact of AI applications [36][37]. - The company has collaborated with various sectors to create tailored datasets that address specific industry challenges, enhancing operational efficiency and service quality [36][37]. Group 5: Future Vision - The company envisions becoming a leading provider of general AI services, focusing on technological advancement, inclusive applications, and an open ecosystem for collaboration [42][43]. - It aims to cultivate a skilled workforce in AI, ensuring that technological innovations translate into practical applications that benefit society [43][44]. - The ultimate goal is to enhance the digital economy while ensuring safety and fairness in AI applications, contributing to a more equitable society [44][45].