Original Summary

I think we’re calling way too many things “AI agents” right now. A lot of products described as agents are really just workflows with an LLM somewhere in the middle. The distinction I’ve been using is pretty simple: Automation = the action is already known. “If X happens, do Y.” Workflow = the process is mostly known. “Do A → B → C, with some branching and AI decisions in between.” Agent = the goal is known, but the system has to figure out the path. “Here’s what I want done. Decide what steps/tools are needed, act, check the result, and continue.” What surprised me while thinking about this is that an agent is often the worse architecture. If a deterministic workflow can solve the problem, it’s usually cheaper, easier to test, and easier to debug. Agents seem much more useful when the task is genuinely openended research, debugging, investigating data, navigating unfamiliar systems, etc. And even then, I think the best systems will probably mix all three rather than making everything autonomous. Curious how other people here draw the line. At what point does a workflow become an agent to you?   submitted by   /u/Active_Pianist_5213 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SaaS
  • 发布时间：2026/10/1 00:57:14