The agent fleet is the part of my work that shows what full AI autonomy looks like in practice. It is a group of AI agents, each with one narrow job, that take a task from request to finished result. A task is written on a board, a plan is made, an agent does the work, a second check looks at the output, and the result is closed with evidence instead of a guess.
The problem it solves is simple. A founder or a small team has more work than hours, and hiring people for every repeated job does not scale. The fleet takes the repeated jobs and runs them, while a decision layer approves the safe changes and sends the risky ones to a person.
Under the hood it is a set of services that talk over internal APIs: a task board, a search index over our own code and documents, a code-generation engine, and routers that send each job to the cheapest model that can do it. Every action is logged so it can be reviewed later.
Status: the dashboards are private because they hold internal data. A public, redacted demo page is planned. I will not quote an agent count until it is read from the dashboard itself.