LangChain 把 AI 循环分成了 5 个清晰的层次，做 Agent 开发或工作流编排的团队可以对照自己的场景找到合适的抽象级别，建议收藏作为架构参考。
LangChain 的 Sydney Runkle 在 X 上分享了关于 AI 循环的 5 种类型：Token 循环（模型生成）、Agent 循环（模型+工具+重复）、深度验证循环（Agent+验证+重复）、部署元循环（事件触发 Agent 运行）以及待完善的引擎循环（通过分析追踪优化系统）。这些循环覆盖了从单次推理到系统级优化的不同抽象层次，帮助开发者理解如何用 LangChain 构建更复杂的 AI 工作流。
Great insights on loops from @sydneyrunkle
Great insights on loops from @sydneyrunkle Sydney Runkle @sydneyrunkle everybody's talking about loops!! how can you instrument them with langchain? 1. token loop supported by a model (choose any model with langchain) 2. create_agent gives you the agent loop (model + tools + repeat until done) 3. deepagents gives you the self verification loop (agent loop + verify + repeat until satisfied) 4. deployments give you the meta loop (trigger agent runs in reaction to events that help improve a system) 5. i think the ??? loop is what we're trying to close with engine: run an agent over each trace and figure out what to tweak - prompts, tools, self verification, etc so that your meta loop is more effective per cycle. 🔗 View Quoted Tweet 💬 3 🔄 0 ❤️ 5 👀 649 📊 2 ⚡