The Web of Agents (WoA) transforms the document-centric Web into an environment of autonomous agents acting on users' behalf, a vision newly tractable as large language models (LLMs) mature. We argue that across three decades the WoA has undergone a \emph{semantic-effort migration} in chronological order: from platform-side coordination (Multi-Agent Systems, Generation~I), through data-side annotation (Semantic Web, Generation~II), to model-side interpretation (LLM-era, Generation~III). The central Gen~II~$\rightarrow$~Gen~III transition within this trajectory, which we call the \emph{semantics-in-data $\rightarrow$ semantics-in-models} shift, is predictive: each generation's failure modes and current open problems follow from where that generation located its semantic effort. The survey makes five contributions: (i)~a unified evolutionary narrative spanning 1990--2026; (ii)~a four-dimensional comparative framework (semantic foundation, communication paradigm, locus of intelligence, discovery mechanism) applied uniformly across all three generations; (iii)~classification of sixteen representative systems on these dimensions, including hybrid LLM--knowledge-graph and computer-use agents; (iv)~coverage of the November~2024--August~2026 institutional convergence (Linux Foundation's Agentic AI Foundation, A2A v1.0, MCP November~2024 launch and November~2025 specification, Visa/Mastercard/Stripe payment-network protocols, EU AI Act phased enforcement, the NIST AI Agent Standards Initiative, International AI Safety Report 2026); and (v)~seven named lessons grounded in cross-generational evidence paired with seven generation-invariant challenges that persist regardless of which protocol prevails. Further progress depends less on protocol design than on the socio-technical infrastructure now being assembled by standards bodies, regulators, and commercial payment networks.

翻译：智能体网络（Web of Agents, WoA）将以文档为中心的网络转变为代表用户行动的自主智能体环境，这一愿景随着大型语言模型（LLMs）的成熟而变得可行。我们论证，在三十年间，WoA按时间顺序经历了一次 *语义努力迁移*：从平台端协调（多智能体系统，第一代），经数据端标注（语义网，第二代），到模型端解释（LLM时代，第三代）。这一轨迹中核心的第二代→第三代转变（我们称之为 *数据中语义 → 模型中语义* 的变迁）具有预测性：每一代的失败模式与当前未解问题均源于该代定位其语义努力的方式。本综述做出五项贡献：（i）一个跨越1990–2026年的统一进化叙事；（ii）一个四维比较框架（语义基础、通信范式、智能定位、发现机制），统一应用于所有三代；（iii）对十六个代表性系统在这些维度上的分类，包括混合LLM-知识图谱与计算机使用智能体；（iv）对2024年11月–2026年8月机构性融合的覆盖（Linux基金会智能体AI基金会、A2A v1.0、MCP 2024年11月启动与2025年11月规范、Visa/Mastercard/Stripe支付网络协议、欧盟AI法案分阶段执行、NIST AI智能体标准倡议、2026年国际AI安全报告）；以及（v）基于跨代证据的七条命名经验，以及七项不依赖于协议胜出的代际不变挑战。进一步进展更多取决于目前由标准机构、监管机构和商业支付网络构建的社会技术基础设施，而非协议设计本身。