Venture(US:VEMLY) Hu Xiu·2025-06-03 01:55Core Viewpoint - The article discusses the current landscape of AI agent platforms, emphasizing the importance of structured knowledge, data quality, and the ability to address model hallucinations for successful implementation and user engagement [58][64]. Group 1: AI Project Hierarchy - The article outlines a seven-layer hierarchy for AI projects based on engineering capabilities, industry know-how, and quality data [3][4]. - The hierarchy ranges from novice users relying on existing tools to advanced industry models that require high costs and expertise [4][11]. Group 2: Agent Platforms - Various agent platforms like Coze, Dify, and Tencent's Yuanbao are evaluated, highlighting their strengths and weaknesses in terms of user experience and data handling [45][51]. - Coze is noted for its user-friendly interface and integration capabilities, making it suitable for simple logic applications, while it struggles with complex logic [40][44]. - Dify is characterized as an open-source platform with a focus on enterprise-level solutions, but it lacks the traffic support that Coze benefits from [46][49]. Group 3: Market Dynamics - The article emphasizes that the success of agent platforms is heavily reliant on traffic generation and user engagement, with a cycle of content creation and user participation driving platform growth [40][58]. - It warns that without substantial industry know-how and effective management of model hallucinations, agent platforms are likely to fail [12][58]. Group 4: Strategic Considerations - Companies are advised to focus on developing unique, structured knowledge that can be monetized and to identify potential customers who value their expertise [60][64]. - The article suggests that merely relying on low-code tools and templates is insufficient for long-term success in the AI space [58][62].