提出分布式智能体系统，实现多智能体在复杂环境下的容错协作。
Distributed Agent System: Fault-Tolerant Collaboration Among Embodied Agents
- 构建设备-边缘-云架构，支持异构智能体协同工作。
- 通过容错对齐与半形式化协议，降低任务累积错误率。
- 适用于工业场景中高可靠性需求的智能体系统开发。
AI工程正从大语言模型的被动文本生成转向以智能体驱动的任务执行，这在资源受限和环境不确定性下带来了新的可靠性挑战。传统纠错优化策略无法解决累积误差传播问题。本文提出分布式智能体系统（DAS），一种面向异构智能体的容错协作设备-边缘-云框架。将智能体可靠性重新定义为系统级容错能力，而非单轮零误差准确率，并设计两层容错架构：基于容错对齐的单智能体执行可靠性，以及基于半形式化语言协议的跨智能体通信可靠性。该框架为工业场景中可靠异构具身智能体协作提供了可落地的工程路径。
原文摘要 · Abstract (English)
AI engineering is shifting from passive text generation by large language models (LLMs) to agent-driven task execution, creating new reliability challenges for long-horizon tasks under resource constraints and environmental uncertainty. Conventional error-elimination optimization strategies fail to address cumulative error propagation. This paper proposes Distributed Agent System (DAS), a device-edge-cloud framework for fault-tolerant collaboration among heterogeneous agents. We redefine agent reliability as system-level fault tolerance rather than single-turn zero-error accuracy, and present a two-layer fault-tolerance architecture: single-agent execution reliability via fault-tolerant alignment, and cross-agent communication reliability via semi-formal language protocols. This framework provides a practical engineering pathway for reliable heterogeneous embodied agents collaboration in industrial scenarios.
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