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系统的关键基础设施，这些系统必须使用工具、与其他智能体协调，并在异构环境中运行。本研究提出了一种受人类启发的视角，通过将智能体通信分为三个层次：通信层、句法层和语义层，来审视这一新兴领域。在此框架下，我们系统分析了18个代表性协议，比较了它们在可靠传输、结构化交互和意义层面协调方面的支持能力。分析显示当前协议设计存在明显的不平衡：大多数协议在传输、流式传输、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提供的协议级机制却十分有限。因此，语义责任往往被推入提示词、包装器或特定应用编排逻辑中，产生了隐藏的互操作性和维护成本。为了将这一差距转化为可操作的建议，我们进一步识别了当今协议生态系统中的主要技术债务形式，并提炼出在不同部署场景下选择协议的实用指南。最后，我们提出了一项研究议程，旨在构建可互操作、安全且语义鲁棒的智能体生态系统，从而超越消息传递，迈向共享理解。