Build a software factory small enough to understand

A tutorial describes how to build a small AI-assisted software factory in GitHub using seven stations that move work from a structured issue to verified production: intake, context, planning, execution, review and policy, delivery, and observability. The approach uses issue templates to define goals, constraints, and completion criteria; a maintained CLAUDE.md file to provide repository context; read-only agents to propose plans for human approval; and coding agents with limited permissions to implement approved work and open pull requests. Independent safeguards include rerunning tests, linting, and type checks outside the agent, enforcing branch protections and policies that prevent agents from changing factory configuration, and using a separate reviewer agent to inspect code and test coverage. After human-approved merges, smoke tests validate the deployed application and can automatically create a revert pull request if failures occur, while prompts, plans, and transcripts are retained as artifacts for traceability. Using a URL-shortening application as an example, the tutorial reports that a change adding 30-day link expiration moved from planning through verified deployment in minutes, while emphasizing that the most important part of an AI software factory is its ability to reject unsafe or incorrect changes at every stage.

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