Agent communication protocols are becoming critical infrastructure for large language model (LLM) systems that must use tools, coordinate with other agents, and operate across heterogeneous environments. This work presents a human-inspired perspective on this emerging landscape by organizing agent communication into three layers: communication, syntactic, and semantic. Under this framework, we systematically analyze 18 representative protocols and compare how they support reliable transport, structured interaction, and meaning-level coordination. Our analysis shows a clear imbalance in current protocol design. Most protocols provide increasingly mature support for transport, streaming, schema definition, and lifecycle management, but offer limited protocol-level mechanisms for clarification, context alignment, and verification. As a result, semantic responsibilities are often pushed into prompts, wrappers, or application-specific orchestration logic, creating hidden interoperability and maintenance costs. To make this gap actionable, we further identify major forms of technical debt in today's protocol ecosystem and distill practical guidance for selecting protocols under different deployment settings. We conclude by outlining a research agenda for interoperable, secure, and semantically robust agent ecosystems that move beyond message passing toward shared understanding.

翻译：智能体通信协议正成为大型语言模型(LLM)系统的关键基础设施，此类系统需使用工具、与其他智能体协调，并在异构环境中运行。本文通过将智能体通信组织为通信层、句法层和语义层三个层次，提出了一种受人类启发的视角来看待这一新兴领域。在此框架下，我们系统分析了18种代表性协议，并比较它们对可靠传输、结构化交互和意义层面协调的支持程度。分析显示当前协议设计存在明显失衡：大多数协议在传输、流式处理、模式定义和生命周期管理方面提供了日益成熟的支持，但在澄清、上下文对齐和验证方面的协议级机制却非常有限。其后果是将语义责任往往推给提示词、封装器或特定应用编排逻辑，从而产生了隐性的互操作性和维护成本。为使这一差距更具操作性，我们进一步识别了当前协议生态系统中的主要技术债务形式，并提炼出不同部署场景下选择协议的实用指南。最后，我们提出了一份研究议程，旨在构建能够超越消息传递、实现共享理解的互操作、安全且语义鲁棒的智能体生态系统。