Modern AI agents can exchange messages using protocols such as A2A and ACP, yet these mechanisms emphasize communication over coordination. As agent populations grow, this limitation produces brittle collective behavior, where individually smart agents converge on poor group outcomes. We introduce the Ripple Effect Protocol (REP), a coordination protocol in which agents share not only their decisions but also lightweight sensitivities - signals expressing how their choices would change if key environmental variables shifted. These sensitivities ripple through local networks, enabling groups to align faster and more stably than with agent-centric communication alone. We formalize REP's protocol specification, separating required message schemas from optional aggregation rules, and evaluate it across scenarios with varying incentives and network topologies. Benchmarks across three domains: (i) supply chain cascades (Beer Game), (ii) preference aggregation in sparse networks (Movie Scheduling), and (iii) sustainable resource allocation (Fishbanks) show that REP improves coordination accuracy and efficiency over A2A by 41 to 100%, while flexibly handling multimodal sensitivity signals from LLMs. By making coordination a protocol-level capability, REP provides scalable infrastructure for the emerging Internet of Agents

翻译：现代AI智能体可通过A2A与ACP等协议交换信息，但这些机制更侧重于通信而非协调。随着智能体规模的扩大，这种局限性会导致脆弱的集体行为——个体智能的智能体却收敛于糟糕的群体决策。本文提出涟漪效应协议，该协调协议不仅要求智能体共享决策结果，还需传递轻量级敏感度信号——即当关键环境变量变化时其决策将如何改变的量化表达。这些敏感度信号在局部网络中如涟漪般传播，使群体能比单纯依赖以智能体为中心的通信更快、更稳定地达成协同。我们形式化定义了REP的协议规范，将必需的消息架构与可选的聚合规则相分离，并在不同激励条件和网络拓扑的场景中对其进行评估。在三个领域的基准测试：(i)供应链级联效应（啤酒游戏）、(ii)稀疏网络中的偏好聚合（电影排期）、(iii)可持续资源分配（渔业银行）表明，REP较A2A协议将协调准确率与效率提升了41%至100%，并能灵活处理来自大语言模型的多模态敏感度信号。通过将协调能力提升至协议层级，REP为新兴的智能体互联网提供了可扩展的基础架构。