Original Summary

AWFlow started as a browser-automation extension (drag-and-drop workflows that act on web pages). Over the last weeks I added agents and teams of agents , and then OpenAI (dots) and xAI (Grok Bot) launched their "AI teammates" a few days apart. Funny timing. Sharing what building it as a solo dev taught me. What it does now create an agent from one sentence: its own model, instructions, memory, skills and rules put agents in a team with a way of working: a lead that delegates, a fixed pipeline, or an open table where you @mention who answers give an agent a routine (every morning, on page load…), which is just a normal workflow you can open and edit everything runs in the browser, on the model you pick per agent (in-browser, Ollama, or your own API key) What I learned Free-form agent chat doesn't scale. Teams only became reliable once handoffs followed an explicit map and each thread had limits on steps, handoffs and cloud tokens. Safety can't live in the prompt. A web page can say "ignore your instructions". Rules (allow / ask / block, plus locked categories like paying or sending) are enforced in code around every tool call. Small models need a lot of help. Lenient JSON parsing, keyword fallbacks, and code that fixes structure instead of asking the model again. Agents and workflows aren't rivals. An agent can run a workflow as a skill, and a workflow can call an agent. Draw the path when you know it; describe the outcome when you don't. Honest limits: no 24/7 cloud computer like the big ones, so it works while your browser is open, and local models are smaller. Disclosure: I build it, it's free. Link + a demo in the comments. Happy to answer anything about the multi-agent runtime.   submitted by   /u/Dannick-Stark [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 · SideProject
  • 发布时间：2026/10/17 11:33:59