Distributed Agent System: Fault-Tolerant Collaboration Among Embodied Agents
2026-07-12 • Multiagent Systems
Multiagent SystemsArtificial Intelligence
AI summaryⓘ
The authors explain that as AI moves from just generating text to actually doing complex tasks, it becomes harder to keep everything reliable over time and with limited resources. They introduce a new system called Distributed Agent System (DAS), which helps different AI agents working together stay fault-tolerant, meaning the whole system can handle errors better. Instead of expecting each agent to be perfect every time, the authors focus on making the entire system reliable by using methods that protect both individual agent actions and their communication. This approach aims to make AI teamwork more dependable in industrial settings.
AI engineeringlarge language modelsagent-driven task executionfault tolerancedistributed systemsheterogeneous agentserror propagationdevice-edge-cloud frameworkcommunication protocols
Authors
Kai Yu, Lu Chen, Hanqi Li
Abstract
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.