构建可演进的智能物理代理网络,解决长期可靠性与互操作难题。
The Internet of Physical AI Agents: Interoperability, Longevity, and the Cost of Getting It Wrong
- 设计支持自主协作的智能代理架构,包含身份认证与安全通信
- 提出语义互操作与策略驱动运行时机制,提升系统可维护性
- 适合关注智能基础设施可持续性的研究者与工程团队
互联网已从连接计算机、人类到数以亿计的物联网设备逐步演进。尽管物联网实现了感知的规模化数字化,却暴露了碎片化、安全性弱、自主性差和长期可持续性不足等根本问题。当前边缘硬件、传感、连接与人工智能的进步,推动进入新阶段:物理AI代理互联网。与仅感知上报的物联网设备不同,物理AI代理具备实时感知、推理与行动能力,在灾害响应、医疗、工业自动化、交通等关键领域自主协同运作。然而,将快速演进的人工智能嵌入长期服役的物理基础设施中,带来互操作性、生命周期管理与过早固化的新风险。本文回顾物联网与互联网演进经验,提出构建弹性、可演进、可信代理系统的架构原则,包括代理身份、安全通信、语义互操作、策略治理运行时与可观测性驱动的治理。强调将演化、信任与互操作作为核心需求,避免将今日假设固化于明日智能基础设施,防止技术与经济成本高昂的失误。
原文摘要 · Abstract (English)
The Internet has evolved by progressively expanding what humanity connects: first computers, then people, and later billions of devices through the Internet of Things (IoT). While IoT succeeded in digitizing perception at scale, it also exposed fundamental limitations, including fragmentation, weak security, limited autonomy, and poor long-term sustainability. Today, advances in edge hardware, sensing, connectivity, and artificial intelligence enable a new phase: the Internet of Physical AI Agents. Unlike IoT devices that primarily sense and report, Physical AI Agents perceive, reason, and act in real time, operating autonomously and cooperatively across safety-critical domains such as disaster response, healthcare, industrial automation, and mobility. However, embedding fast-evolving AI capabilities into long-lived physical infrastructure introduces new architectural risks, particularly around interoperability, lifecycle management, and premature ossification. This article revisits lessons from IoT and Internet evolution, and articulates design principles for building resilient, evolvable, and trustworthy agentic systems. We present an architectural blueprint encompassing agentic identity, secure agent-to-agent communication, semantic interoperability, policy-governed runtimes, and observability-driven governance. We argue that treating evolution, trust, and interoperability as first-class requirements is essential to avoid hard-coding today's assumptions into tomorrow's intelligent infrastructure, and to prevent the high technical and economic cost of getting it wrong.
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