Core Viewpoint - The article discusses the transformation of traditional manufacturing through the implementation of industrial AI, specifically focusing on Geega's industrial AI platform developed by Geely Group, which aims to enhance efficiency and address challenges in the automotive industry [4][6]. Group 1: Industrial AI Implementation - Industrial AI is seen as a key solution to the structural challenges faced by traditional manufacturing, particularly in the context of real-time decision-making and dynamic optimization [6][10]. - Geega's industrial AI platform has been successfully implemented in Geely, Lynk & Co, and Zeekr factories, resulting in significant improvements such as a 15% reduction in order delivery cycles and a 13% decrease in quality loss costs [4][16]. Group 2: Platform Development Stages - The development of the Geega platform involves four key stages: 1. Standardization of industrial software to enable reuse and promotion [12]. 2. Construction of the GOS (Geely Operating System) to unify the computing environment and application portal [14]. 3. Launch of the AI application platform and business agents to facilitate human-machine collaboration [15]. 4. Establishment of a factory brain for continuous improvement through a PDCA mechanism [15][18]. Group 3: Practical Applications and Outcomes - In the Chengdu factory of Lynk & Co, the implementation of an integrated digital base led to a 13% reduction in quality loss costs, a 10% increase in logistics efficiency, and a 15% decrease in order delivery cycles [16]. - The Ningbo factory of Zeekr saw a 10% reduction in equipment failure rates and an 11% increase in equipment utilization through the establishment of a fully connected factory base [17]. Group 4: Intelligent Agent Architecture - The architecture of the Geega platform's intelligent agent system consists of three layers: perception, decision-making, and execution, enabling comprehensive monitoring and intelligent decision-making [18]. - The platform offers standardized industrial components and modules that can be easily integrated using a low-code approach, allowing users to customize applications without extensive technical knowledge [21]. Group 5: Methodology and Experience Summary - The article emphasizes three key points for constructing intelligent agents: 1. Starting from specific business problems to ensure AI is embedded in practical workflows [30]. 2. Defining data and system modifications based on identified issues to enhance decision-making capabilities [33]. 3. Fostering continuous evolution by accumulating knowledge and experience from each intelligent agent's deployment [34].