Core Viewpoint - The barriers to the domestic market for Agents are being removed, with a significant turning point expected in the Chinese to B Agent market by Q1 2026, benefiting industry giants like Alibaba Cloud and fostering the growth of their ecosystems [2][3]. Market Demand - The commercial adoption of Agents is driven by clear market demand, improved product supply, and a recovering primary market. The return on investment (ROI) for Agents is becoming more evident, with a downward trend in token prices in the U.S. market encouraging enterprises to adopt Agents [3][4]. - In the domestic market, the willingness to pay for Agent software has been low due to various factors, but as API usage costs decrease, Agents will provide clearer cost advantages, leading to an increase in ROI and rapid adoption in China [3][4]. Product Supply - Technological advancements are enhancing delivery capabilities, allowing higher-quality Agent products to enter the market. Agents differ from traditional software by providing direct data results based on simple natural language commands, streamlining processes [4][5]. - The release of DeepSeek-R1 has improved the capabilities of domestic models, with expectations of further advancements by the end of this year or early 2026, strengthening the delivery capabilities of Agents [4][5]. Primary Market - The recovery of investment and financing in the primary market is expected to support the flourishing of the to B Agent market by early 2026. The capital input and product innovation have entered a new phase since Q2 of this year, with results anticipated within approximately six months [5][6]. Industry Opportunities - Agents are expected to first land in sectors such as law, finance, and customer service, where data standardization and high labor costs make ROI more favorable. Industry giants like Alibaba Cloud will gain significant advantages in these areas [5][6]. - The delivery of Agent products must enhance client efficiency, necessitating knowledge of vertical scenarios and product ROI [5][6]. Model and Hardware Ecosystem - Large models are central to AI Agent applications, but successful implementation requires a comprehensive solution encompassing applications, training data, computing power, and engineering execution. The integration of hardware and software ecosystems is crucial [6]. - Industry giants' self-developed chip capabilities can significantly reduce inference costs, with chip costs accounting for 60% to 70% of overall AI cloud service costs. Successful self-developed chips could greatly enhance overall gross margins [6]. - The variety of models and partnerships among industry giants facilitates easier access to B-end clients, as enterprises typically select multiple targeted AI Agents based on application scenarios [6].