Agent 正在终结云计算“流水线”，Infra 必须学会“思考”

Core Viewpoint - The article discusses the transition from traditional AI infrastructure to a new paradigm called "Agentic Infra," which is essential for the scalable deployment of intelligent agents in various industries [2][3]. Infrastructure Evolution - The evolution of infrastructure is moving from AI Infra to Agent Infra and then to Agentic Infra, which is crucial for the large-scale implementation of intelligent agents [2]. - The infrastructure must evolve from a "production line factory" to a "solution company" to support the quality of tasks executed by agents [3][4]. Key Upgrades Required - Multiple dimensions need to be upgraded, including flexible execution environments, comprehensive tools for agents, precise contextual information, and robust security and monitoring mechanisms [4]. - The infrastructure must coordinate continuous and interrelated tasks, emphasizing the importance of sandboxing and flexible scheduling capabilities [4]. Shift in Focus - The focus has shifted from "calculating faster" to "thinking longer," requiring different types of resources for thinking and calculation [5]. - The current bottleneck lies not in the models themselves but in the supporting infrastructure's responsiveness [6]. Challenges in Agent Deployment - The decline in user numbers for platforms like Lovable indicates that while initial interest may be high, sustained engagement is challenging due to unmet user expectations [5]. - The core issue is that while agent models are capable, the supporting infrastructure and tools are still immature [6]. Future of Agentic Infra - The goal is to create an advanced Agentic Infra that allows for better resource integration and innovative functionalities, leading to a virtuous cycle of technology and application development [7][10]. - The infrastructure should enable agents to autonomously design workflows, moving from being viewed as tools to collaborators [12][13]. Technical Innovations - The introduction of micro-virtualization and sandbox management mechanisms aims to optimize resource allocation and utilization, addressing inefficiencies in traditional AI infrastructure [16]. - Unified scheduling of heterogeneous computing resources is a key innovation, allowing for better performance and efficiency [17][18]. Industry Integration - The transition from technical breakthroughs to industry integration is crucial, focusing on usability and performance rather than underlying hardware differences [18]. - The company aims to provide a robust AI-native infrastructure that supports clients in focusing on product iteration while managing complex backend operations [19][20]. Vision for the Future - The vision includes a future where intelligent agents collaborate to complete complex tasks, significantly enhancing productivity and creativity [14][22]. - The company aspires to be a foundational engine for AGI development, facilitating the transition to a more intelligent and autonomous infrastructure [22].