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How to Build an AI Software Factory
An AI software factory is a pipeline where agents propose and deterministic gates and accountable humans dispose. Here is how we follow one change through seven stages.
Passing tests do not prove a page works. Here is how we make AI agents open the real page, read the screenshot, and fix the browser path until proof is fast.
Our ai agent dispatcher never starts sibling chats. It prints a copy-paste packet and keeps a ledger, while a separate supervisor runs bounded workers inside its own session.
Some product boundaries are too important for an agent's judgment. Here is how we encode them as ai agent compliance guardrails: status labels, a checklist, escalation by default, and a golden test.
Risk based code review for AI agents means deterministic path rules set the floor, agent evidence can only raise it, and humans stay required where a miss costs the most.
Two agents with opposing roles can ping-pong forever, and an agent that resolves its own threads grades its own homework. Here are the caps, escalation triggers, and defaults we use.
To reduce AI agent token cost, stop paying for turns that decide nothing. A tested script, a wake gate, and a pre-wake filter make silence the default output.
Our agents used to push and stop while a human relayed CI failures and review comments. Here is the ai agent pull request workflow we built, with GitHub as the bus and completion loops.
We built an automated AI code review GitHub Action in one day and reverted it the same day. Here is what we built, what the failures showed, and what we do instead.
A developer burned a token budget on one multi-agent review, so we measured where the tokens went. Here is what an AI code review costs, what cut it, and what did not.
We split AI code review into a deterministic gate, fresh single-purpose reviewers and a computed tally, because one long-running reviewer skims its checklist and never asks the big questions.
How we test AI agent instructions like code: reproduce a missed rule from a real review, encode a bad and a good answer, score both with a rubric, and keep the case forever.
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