What is an AI software factory? A plain-English guide from someone who runs one

Most people use AI for code like a very fast intern. They open a chat, paste a task, wait, fix what comes back, and repeat. At the end of the day they feel busy and not much is shipped. An AI software factory is the opposite setup: work flows through a system, and you only touch the start and the end.

What an AI software factory actually is

A software factory is not a product you install. It is a way of organizing agents so software moves from request to release without you pushing every step. Firecrawl describes it as the system around a coding agent rather than the agent itself: a queue of work, isolated workspaces, automatic verification and a human at the merge gate. Augment Code frames it the same way: agents write, test and ship code, while specifications and merge approvals stay under human control.

That matches what we run. The honest test is simple: if you removed the agents, would you still have a process? If yes, you have a factory. If no, you have a chat window.

The five parts every factory needs

1. A source of work. A task board with clear tickets. Agents should pull work from it, not wait for you to type prompts.
2. Someone who assigns. A manager role that breaks a goal into tasks and hands each to a worker.
3. Workers with their own space. Coding agents that each have an isolated place to edit and run code. See how to run multiple coding agents in parallel.
4. Independent checks. Review and testing done by a different agent than the one that wrote the code. See AI code review and testing agents.
5. A release step with a human gate. Staging first, then a person confirms before it goes out.

How we do it at SupaKid Studios

This is the real setup Tomi walks through in his factory video. He spent about $20,000 on it in one month, rebuilding several startups and games for himself and for clients.

At the top is a personal agent, J.A.R.V.I.S., that acts as chief of staff. Under it sit PMs. There is one PM for each business concept or client project, each with its own task board. Next to each PM is an engineering manager. Under the engineering manager sits as many sub agents as the work needs.

The sub agents write the code and open a pull request. A separate software engineering agent, Devin, then does QA on that pull request using its own computer. The engineering managers hand out work to the sub agents, and Devin's cloud agents review code and test apps on their own machines.

Three habits make it work:

• Managers wake up every 30 minutes. Each PM and engineering manager checks the board, sends down new sub agents with defined tasks, and updates the status so Tomi can see where things stand.
• Sub agents are disposable. When a sub agent merges code or finishes a piece of content, it is archived and a new one takes the next task.
• Tomi talks to one agent. He mostly talks to J.A.R.V.I.S., not to the engineering managers. J.A.R.V.I.S. delegates everything underneath.

Why bother: context switching is the cost

The reason for this shape is not speed alone. When you work with many agents directly, you switch context all day. You finish feeling like you did a lot of work and still did not get what you needed done. Tomi's rule is that the human should only define the input and review the output. Software is a good fit because it is verifiable: it works or it does not.

The loop also needs to be small and fast. If what comes back is not what you expected, the fix is a better input, not more hours of watching.

Build a small version this week

You do not need $20,000. Tomi says you can run a factory like his with a strong lead model and a set of sub agents natively in Claude Code. Claude Code's own docs describe several ways to run agents in parallel, including subagents and agent teams with a shared task list.

1. Pick one project and write its board: ten tickets, each with a clear "done" line.
2. Create one manager role (a prompt, not a person) whose only job is to read the board and assign work.
3. Let two coding agents work on separate tickets in separate branches.
4. Add one reviewer agent that never wrote the code.
5. Release to staging only. You check it and decide.
6. Once that loop works, add a second PM for a second project.

What goes wrong

• No definition of done. Agents ship something that looks finished and is not. Write the check before the task.
• One agent grading its own homework. The writer and the reviewer must be different.
• Too many agents too early. Review speed is your limit, not agent count.
• Skipping the human gate. Tomi still reviews staging and verifies QA before rollout. Review, merge and rollout are separate steps.

Want one set up for you?

SupaKid Studios sets up and runs this kind of factory for teams, with a studio team managing the agents and your people approving what ships. It is the same approach we use for our own products and for a managed AI agent team. If you want to see what it would look like for your product, book a 30-minute call.