Multi-agent systems powered by Large Language Models face a critical challenge: agents communicate through natural language, leading to semantic drift, hallucination propagation, and inefficient token consumption. We propose G2CP (Graph-Grounded Communication Protocol), a structured agent communication language where messages are graph operations rather than free text. Agents exchange explicit traversal commands, subgraph fragments, and update operations over a shared knowledge graph, enabling verifiable reasoning traces and eliminating ambiguity. We validate G2CP within an industrial knowledge management system where specialized agents (Diagnostic, Procedural, Synthesis, and Ingestion) coordinate to answer complex queries. Experimental results on 500 industrial scenarios and 21 real-world maintenance cases show that G2CP reduces inter-agent communication tokens by 73%, improves task completion accuracy by 34% over free-text baselines, eliminates cascading hallucinations, and produces fully auditable reasoning chains. G2CP represents a fundamental shift from linguistic to structural communication in multi-agent systems, with implications for any domain requiring precise agent coordination. Code, data, and evaluation scripts are publicly available.

翻译：基于大型语言模型的多智能体系统面临一个关键挑战：智能体通过自然语言进行通信，导致语义漂移、幻觉传播和低效的令牌消耗。我们提出G2CP（基于图结构的通信协议），这是一种结构化的智能体通信语言，其中消息是图操作而非自由文本。智能体在共享知识图谱上交换显式的遍历命令、子图片段和更新操作，从而实现可验证的推理轨迹并消除歧义。我们在一个工业知识管理系统中验证了G2CP，其中专业化智能体（诊断型、流程型、综合型和知识摄取型）协同工作以回答复杂查询。在500个工业场景和21个实际维护案例上的实验结果表明，与自由文本基线相比，G2CP将智能体间通信令牌减少了73%，任务完成准确率提高了34%，消除了级联幻觉，并生成了完全可审计的推理链。G2CP代表了多智能体系统从语言通信到结构通信的根本性转变，对任何需要精确智能体协调的领域都具有重要意义。代码、数据和评估脚本均已公开提供。