用企业级智能体落地，还有谁没踩这四种大坑？无问芯穹的系统性解法来了

Core Viewpoint - The article discusses the challenges and opportunities in the implementation of AI agents in enterprises, emphasizing the need for a robust infrastructure to support their effective deployment and operation [4][52][63]. Group 1: Current State of AI Agents - AI agents have been integrated into many workflows but are often perceived as having only intern-level capabilities [2][3]. - Many teams use AI agents for automation but do not fully trust them with core responsibilities [3][4]. - The focus in the industry is shifting from merely achieving model performance to addressing engineering and application scenarios for enterprise-level deployment [4][52]. Group 2: Challenges in AI Agent Implementation - Enterprises face four common pitfalls when deploying AI agents: effectiveness issues, stability during scaling, rising costs, and difficulties in establishing a commercial loop [8][21]. - Effectiveness issues arise from various factors such as model selection and prompt design, leading to performance degradation over time [11][12][13]. - Stability problems become apparent when AI agents transition from small-scale trials to real business environments, resulting in task delays and errors [14][15]. - Despite expectations, AI agents have not significantly reduced costs, with high token usage leading to expenses of 20-50 yuan for large model calls [16][17][18]. - Establishing a commercial loop requires AI agents to integrate into product flows and payment systems, which many current solutions lack [19][20]. Group 3: Solutions Offered by Wenshu Qiong - Wenshu Qiong's AI agent service platform aims to address the systemic gaps in AI agent deployment [25][26]. - The platform provides a comprehensive solution that includes templates for various AI capabilities, allowing enterprises to avoid trial-and-error during initial implementation [28]. - It offers stability and scalability through robust technical support and system resilience, significantly improving operational efficiency [32][33]. - Cost management is enhanced through deep integration of model optimization and hardware collaboration, allowing enterprises to control expenses effectively [36][37][39]. - The platform facilitates commercial viability by connecting AI agents with external tools and payment systems, streamlining the integration process [41][42]. Group 4: Future Trends and Organizational Changes - The article predicts that as AI agents become more prevalent, enterprises will need to adapt their organizational structures to accommodate multiple agents working collaboratively [55][56]. - The competitive edge will increasingly depend on the number and quality of AI agents and their collaborative systems within organizations [60][61]. - The infrastructure for AI agents will be crucial for differentiating enterprises in the market, akin to the foundational systems that support vehicles [61][62]. - Wenshu Qiong positions itself as a provider of this essential infrastructure, focusing on creating a solid foundation for enterprise-level AI agent deployment [63][67].