多智能体系统故障多由并发控制缺失引发，需显式管理共享状态访问。
Position: Multi-Agent Systems Should Prioritize Concurrency Control
- 将多智能体系统故障归因于经典并发异常，如脏读、丢失更新。
- 提出通过冲突检测与资源隔离提升系统可靠性，避免状态不一致。
- 适合研究多智能体协作与系统可靠性的开发者参考。
基于大模型的多智能体系统（MAS）虽具备可扩展协作潜力，但增加智能体常导致可靠性下降。本文认为，多数故障本质上是并发控制问题：智能体并发读写共享状态，且大模型推理延迟放大了过期读取、丢失更新和结果不一致的风险。常被归因于协调或通信失败的现象，可直接映射到经典的并发异常。我们主张，多智能体框架应通过显式并发控制机制（如冲突检测、隔离保障、结构化资源访问）来应对这些问题。并发控制应作为核心设计考量，而非事后补救。
原文摘要 · Abstract (English)
LLM-based multi-agent systems (MAS) promise scalable collaboration, yet adding agents often reduces reliability. This position paper argues that many MAS failures are fundamentally concurrency control problems: agents concurrently read and write shared state, and long LLM inference windows amplify the risk of stale reads, lost updates, and inconsistent outcomes. Failure modes commonly attributed to coordination or communication breakdowns can be mapped directly onto classical concurrency anomalies. We contend that MAS frameworks should address these failures through explicit concurrency control mechanisms: conflict detection, isolation guarantees, and structured access to shared resources. Concurrency control should be a first-class design concern, not an afterthought.
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