Core Viewpoint - The core discussion at the China-Germany Intelligent Manufacturing Technology Innovation Cooperation Forum focused on "Industrial Large Models and Industrial Intelligence," highlighting the challenges of applying general AI models in industrial settings [1] Group 1: Challenges of General AI Models - General AI models often struggle in factory environments due to the need for precision, deep expertise, and real-time responsiveness, which are not adequately met by these models [1] - The lack of deep industry knowledge in general models leads to unreliable outputs, and their response speed may not keep pace with high-speed production lines, negatively impacting efficiency [1] - The cost of achieving marginal performance improvements in industrial applications can be disproportionately high, making AI solutions impractical for certain scenarios [1] Group 2: Solutions through Multi-Agent Systems - CloudWalk Technology proposes a practical solution by developing a multi-agent system that acts as a "digital expert team" to address industrial challenges [2] - The collaboration with Qingshan Industry has resulted in ten specialized intelligent agents that enhance operational efficiency by performing distinct roles while working together [2] - Key agents include a "Knowledge Management Expert" that reduces document query response time from 15 minutes to under 3 seconds, and a "Production Quality Expert" that accurately analyzes repair data to identify quality issues [2] - The intelligent agents operate within a layered social network architecture, creating a complete feedback loop that improves overall efficiency in production processes [2]