https://devopstoolkit.live/development/how-i-manage-45-ai-agents-across-3-machines/index.htmlRight now, I have forty-five agents working across three machines: my laptop, a mini PC in my office, and a server in InMotion Cloud. I call it my agent fleet. In a few hours, that fleet produces what would take me a week or two to write myself. Getting it to do the work is not the hard part anymore. Seeing what it’s doing is. We all know how to run an agent and work with it. Many of us know how to do the same with a few agents running in parallel. But what happens when there are dozens of them, spread across multiple machines? How do we manage a fleet like that? Because that’s what I am these days. A manager. And a good manager’s work comes down to three things: delegating work, resolving conflicts, and removing obstacles so the team can keep going. To do that well, a manager needs a team that can work on its own most of the time, and a way to see what that team is doing when it can’t. With AI, that team is made of agents. They’re already very good at almost everything, and if I did my part, they know how I like to work, what the rules are, and what they should and shouldn’t do. That’s what AGENTS.md and skills are for. I’ll assume those are in place, since that’s a whole different topic. What I want to show you is how that team grows. From a single agent I have to babysit, to teams of agents spread across machines I don’t even need to be near. And what happens to the manager along the way, because the agents scale much better than I do. The beginning is often the best place to start, so let’s go back a year or two. It would be close to impossible to reproduce the exact steps from this post. They depend on my machines, my repositories, my issues and my agent setup. Instead, try something similar with Agent Deck: start with one agent, add a dispatcher, and grow from there onto additional machines.Hugoen-us