在过去一年里，我们见证了LLM (大语言模型) 爆发式的增长，LLM的能力有了质的飞跃，也颠覆了所有开发者对“软件能力边界”的认知。只需要几行代码，调用一次LLM api接口，模型就能帮你写一段看起来像模像样的代码、总结一份结构清晰的文档或者回答一些“看起来很聪明”的问题。但当你试图想构建一个稳定、可复用、复杂的生产级别的AI应用时就会遇到

• Prompt 失控

  一开始只是几行提示词，后来变成了几百行规则说明。你不断往 Prompt 里“打补丁”，但模型依然会在某个边缘场景下给你一个完全不可用的结果。

• 结果不可预测（Non-deterministic）

  LLM 是概率模型，而业务系统追求的是确定性。

  “大概率对”在 Demo 阶段可以接受，但在审批、风控、数据查询这类场景中，等同于事故隐患。

• AI应用开发周期长，不能复用

  新做一个 AI 应用，往往要重新写一套流程代码，反复的在从零开始造轮子。

• 调试困难

  当用户反馈“刚刚还能用，现在不行了”，你却无法复现。

  你不知道当时：

  • Prompt 最终渲染成了什么
  • 模型具体返回了哪一步异常
  • 是模型波动，还是上下游数据变化

这些问题叠加在一起，会把一个原本看起来“很有前途”的 AI 项目，迅速拖入不可维护的深渊。这也是AIWorks平台诞生的初衷，AIWorks不仅仅是一个简单的低代码开发工具，它是一个确定性的编排系统。本文将从工程师的角度带你了解一下AIWorks平台中workflow的设计与实现。

核心设计哲学

我们将Workflow引擎的设计，收敛为四个核心原则

DAG为骨架

复杂的业务逻辑，如果不加整理，往往是一团乱麻的代码（Spaghetti Code）。我们将业务逻辑抽象为数学上的 DAG（有向无环图）。

• Node（节点）：代表一个原子的计算单元。它可能是一次 LLM 的推理，也可以是一段 Python 代码的执行，或者是一次 HTTP 请求。
• Edge（边）：代表数据的流向和执行的顺序。

这种设计使得业务逻辑可视化。前端拖拽生成 JSON，后端解析执行 JSON。所见即所得，对非技术人员来说非常的友好。

状态机为灵魂

很多工作流系统，本质上只是任务编排器，节点按顺序执行，执行完就结束。但AI Workflow的行为模式具备以下特征：

• 多轮交互
• 上下文强依赖
• 当前行为取决于“之前发生了什么”

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