Building an AI software factory is mostly a matter of putting the right stages around the coding agent you already run, so a task can move from a request to a change deployed on your cloud without a person hand-writing infrastructure or reading every line. Teams reach for it at a specific point, when agents start producing more changes than anyone can review and the review time swallows the speed that generation was supposed to buy; AI-authored pull requests already wait 4.6 times longer for a first review (LinearB). None of what follows is a rebuild, and you add the stages one service at a time, starting from what you already run.

Start from one existing service

You begin from a service you already have rather than a blank repository, declaring its infrastructure in the code so the factory can read what a change needs and stand it up on demand. In practice you wrap the resources the service already uses, a database here or a queue there, in an infrastructure SDK. The example below uses Encore's SDK, one implementation of this pattern:

import { SQLDatabase } from "encore.dev/storage/sqldb";

const orders = new SQLDatabase("orders", { migrations: "./migrations" });

export async function createOrder(req: CheckoutRequest) {
  const order = await orders.queryRow`
    INSERT INTO orders (user_id, total)
    VALUES (${req.userID}, ${req.total})
    RETURNING *
  `;
  return order;
}

The query and the business logic are unchanged, and with the database now declared in the code the factory can read that declaration, create a copy of it for a preview environment, and provision the same resources in production. You onboard a service this way without a rewrite, and it keeps running on the cloud account, VPC, and database it already uses.

Give each agent an environment that matches production

With infrastructure declared in code, each agent gets an environment of its own, created for the task and torn down after, so twenty can run at once without one agent's migration breaking another's test run. That environment comes up with the same database and queues the service runs in production, so the agent builds against what will actually ship rather than against mocks, and it catches the bugs that live in the database and the calls between services, where an agent running only unit tests never looks.

A bare sandbox platform isolates agents but leaves you to stand up the database and queues yourself; a shared staging environment has the infrastructure but loses the isolation that keeps parallel agents from colliding.

Make validation something a reviewer can trust

When an agent hands back a change, someone still has to know it works, and reading an unfamiliar thousand-line diff to find out is slower than the generation was fast. Each change should arrive with a running environment attached, so a reviewer opens it, exercises the feature, and checks traces when something looks wrong, then merges on the strength of having seen it work. Skip it, and the pile-up moves from writing code to reviewing it rather than clearing it.

Let validated changes deploy on their own

A merged change should reach production without an agent improvising cloud configuration, so the factory derives the infrastructure from static analysis of the code and generates the same IAM, networking, and provisioning every time, which keeps the deploy off the agent's judgment and lets you see what a change will create by reading its declarations rather than a generated script someone has to check. The limits it runs inside belong to the platform team, set once in a control plane rather than negotiated per change, so agents move quickly within approved policy while a person stays at the merge as the one gate to production. How to let agents deploy safely to AWS or GCP covers that stage on its own.

Turn it on gradually

Rather than switching all of this on at once, start with low-risk tasks on the one or two services you onboarded, keep a person at every merge, and widen scope as validation earns trust, measuring progress by whether agent output is cheap to confirm rather than by how much of it there is. A factory is worth the setup once agents produce more than the team can comfortably review, or once standing up infrastructure for each experiment is the thing slowing them down; below that, a preview environment and your existing pipeline are enough.

Start small: onboard one service by declaring its infrastructure in code, point an agent at a low-risk task, and review what comes back in its own environment. Encore's docs walk through that first onboarding and deploy.

For the idea behind this, see what is an AI software factory; for the architecture, AI software factory architecture; and for how the team's day changes, how engineering teams work in an AI software factory.