Comment by rigonkulous

11 hours ago

I have started sandboxing all AI's in their own VM, and interfacing with them primarily through Jira and Git.

It really is the only thing that makes sense. Completely sandbox'ed, and treated like the junior programmer who will do, literally, any dumb thing you tell them to do, as long as there is an Issue for it.

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rigonkulous

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resonious  11 hours ago

I do a similar thing where the agent runs in a Docker container and I talk to it with Telegram. It has GitHub CLI access but only with a very restricted PAT. No bind mounts. Jira is pretty clever, though I'm not feeling enough pain with just Telegram to want to try switching at this point.

  • rigonkulous  9 hours ago

    I have multiple relatively well-established Jira projects I've been able to add agents to, and also clone/use as a template for new agent-only projects which give me another kanban to manage, pretty comfortably ..

    The big thing about my Jira use besides the fact that its a historical tool into which I've integrated agents, is that managing agents through Jira's custom workflows is really, really cool. You can actually do any of the old workflows with agents - they'll just do it. Finally, effective waterfall! ;) *Just kidding, I've always been able to do waterfall properly...

mapBasketWand  10 hours ago

Why are you using Jira and not GitHub issues?

  • rigonkulous  9 hours ago

    One thing that is vital to managing agents this way, is the ability to easily create and use workflows, which can be easily kanban'ed. This is where I find Jira very comfortable - plus, I am already accustomed to managing Jira with both a workflow-driven Kanban, and jira-cli, the command-line interface.

    So actually, firing off commands with jira-cli to get flows started by multiple agents watching their issues and putting their work in issue threads, is quite a nice interface .. and compatible with the other human-powered projects I'm managing this way, also.