对比LLM智能体通信协议在任务编排中的表现，量化其效率与成本差异。
Empirical Comparison of Agent Communication Protocols for Task Orchestration
- 构建三层次基准测试，比较工具集成、多智能体协作和混合架构
- 在复杂任务中，混合架构响应时间最短，但上下文消耗最高
- 适合研究智能体协作机制或优化系统成本的开发者参考
研究大型语言模型（LLM）智能体在任务编排中通信协议的对比评估问题。重点考察智能体与外部工具之间，以及自主智能体之间的交互过程。目标是建立一个系统的初步基准，针对标准化查询，在三个复杂度层级上比较工具集成、多智能体委派和混合架构的性能。通过量化响应时间、上下文窗口使用量、成本、错误恢复能力及实现复杂度，揭示各方案的优势与不足。
原文摘要 · Abstract (English)
Context. The problem of comparative evaluation of communication protocols for task orchestration by large language model (LLM) agents is considered. The object of study is the process of interaction between LLM agents and external tools, as well as between autonomous LLM agents, during task orchestration. Objective. The goal of this work is to develop a systematic pilot benchmark comparing tool integration, multi-agent dele-gation, and hybrid architectures for standardized queries at three levels of complexity, and to quantify the advantages and disadvantages in terms of response time, context window consumption, cost, error recovery, and implementation complexity.
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