running-the-fleet
People assume running a fleet of AI agents means constant supervision, a human frantically watching a dozen robots. It is the reverse. The human's job shrinks to almost nothing, on purpose, and that shrinking is what makes it work at all. The operator here does one thing: approve
running-the-fleet
If every repository in the fleet is an employee with one job, they still have to coordinate. The publishing repo needs to know a feature shipped. The ops repo needs to tell someone an alarm fired. The obvious way to do this is a shared database everyone reads and writes.
running-the-fleet
Every repository in our fleet carries a full copy of the same operating framework. Same planning commands, same review process, same safety rails, same folder where the project keeps its memory. We install it into each repo the way a company images every laptop with the same operating system before
running-the-fleet
Here is what actually happens when we want a new feature, start to finish, with AI agents doing essentially all of the work. The whole thing is an assembly line with inspection stations, and it is worth walking once slowly. It starts with a sentence from the human: "I
running-the-fleet
If you have played with AI coding agents, you have probably run one agent in one project and watched it do one thing. Running a whole fleet of them sounds like that, times ten, times chaos. It is not. The trick that makes it sane is almost embarrassingly simple: stop