📄 中文摘要
在多智能体人工智能的实践中，直接的代理间调用会导致分布式单体架构，因此建议使用消息总线。大型任务容易出现幻觉，采用小规模的顺序生成并在每个阶段进行审查会更高效。服务器资源有限，需合理匹配工作负载与硬件，避免内存溢出。共享知识与私有记忆的结合能够提升系统的灵活性，不同的信息不应混合在一起。此外，代理可能会出现故障，因此在设计时应考虑到这一点。作者在三台机器上运行八个AI代理，处理质量保证、语音AI、广告创意、知识管理和面试分析等任务，经过两个月的实践总结出这些有效的协调模式。
多智能体人工智能：我艰难学习的五种协调模式
出处: Multi-Agent AI: 5 Coordination Patterns I Learned the Hard Way
发布: 2026年2月26日
📄 English Summary
Multi-Agent AI: 5 Coordination Patterns I Learned the Hard Way
In the practice of multi-agent AI, direct agent-to-agent calls can lead to distributed monoliths, so using a message bus is recommended. Large tasks tend to hallucinate, and employing small sequential spawns with reviews at each stage proves to be more efficient. Limited server resources necessitate matching workloads to hardware to avoid out-of-memory errors. Combining shared knowledge with private memory enhances system flexibility, and not all information should be mixed together. Additionally, agents may fail, so this should be accounted for in the design. The author runs eight AI agents across three machines, handling tasks such as QA, voice AI, ad creative, knowledge management, and interview analysis, summarizing these effective coordination patterns after two months of practice.