Original Summary

I've been experimenting with AI coding agents, but I kept running into the same question: What happens when the agent doesn't start from a blank environment every time? So I built a small persistent workspace for Hermes Agent . The idea was pretty simple: instead of giving the agent a fresh project for every session, I gave it a workspace with: Existing files and project structure A Git repository Notes and task files Python project files Tests A place where changes could persist between sessions I then asked Hermes to work on a small Python CLI project. It created the project, ran it, wrote documentation, and verified the functionality. The interesting part came later. I ended the session and started a fresh Hermes session , then asked it to continue working on the existing project. It could see the files and Git state from the previous session and make another change instead of starting over. That made the workflow feel quite different from a normal chatbot conversation. The basic workflow became: Persistent workspace → Agent → Files → Code → Tests → Git state → Next session What I learned is that persistence changes the role of the agent . It's no longer just: "Give me a prompt and generate some code." It starts becoming: "Here's the project. Figure out where we left off and continue." Of course, this was a small experiment, not a production autonomous development environment. I wanted to understand the basic workflow before making it more complicated. I'm curious how others are approaching this: Do you maintain persistent workspaces for your AI coding agents, or do you prefer starting each agent session from a clean environment? I documented the experiment and the setup here: https://medium.com/@techlatest.net/i-built-a-persistent-ai-developer-environment-with-hermes-agent-912116a29088?sharedUserId=techlatest.net Would especially like to hear from people using agents across multiple sessions.   submitted by   /u/techlatest_net [link]   [comments]

中文概览

中文标题： 我为AI编码代理搭建了持久化工作区——我的收获

作者为Hermes Agent搭建了持久化工作区，包含现有文件与项目结构、Git仓库、笔记与任务文件、Python项目文件和测试，使改动可在会话间保留。作者让它处理一个小型Python CLI项目，它能创建、运行、写文档并验证；结束会话后新开会话仍能读取文件和Git状态继续修改。作者认为持久化改变了代理的角色，从“生成代码”变为“接续项目”，并询问他人是否也维护持久工作区。

  • 情报分类：技术学习与提效
  • 分类依据：关于AI编码代理持久化工作区的实验与经验分享
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/8 18:08:12