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]