AI coding governance

What is AI coding governance?

AI coding governance is the set of controls that sit around AI-written code before it reaches your repository. It does not replace the model or your code review. It decides when an agent may start, checks what it produced, and keeps a record of every step. In practice it comes down to four controls:

Why AI-generated code needs governance

Running an agent directly on a repository works well for one developer at a desk. It gets harder when agents work unattended, on production repositories, for a whole team:

• The scope is decided too late. An agent that starts editing straight away shows you what it chose to change only at review time, when a large diff is already written.
• "It works" is the agent's opinion. Unless the change is run against the repository's real checks, a failing change can still reach a pull request and land in a reviewer's queue.
• Secrets and network are within reach. An agent running in a developer's environment can see whatever that environment can see.
• There is no record. When something goes wrong later, terminal history is rarely enough to show who approved what, and why.

AI coding governance vs AI code review

The two are often confused, and they work best together. AI code review tools read a pull request after it exists and comment on it. Governance acts earlier and around the whole change: it controls whether the work starts at all, what environment it runs in, and whether a pull request may be opened. A governed change still goes through your normal code review — governance makes sure that what reaches review was planned, approved, and passing.

How Dualzo governs AI coding agents

Dualzo is an AI development governance platform for GitHub. It applies the four controls to every change an AI agent makes, in any language:

1. You label a GitHub issue or write a prompt.
2. The model you chose — Claude, Codex, Gemini, or Grok — writes a plan from the issue and what Dualzo knows about your repository.
3. Your team gets the plan by email, Slack, Microsoft Teams, Google Chat, or Discord, and a person approves it.
4. The plan runs one task at a time on a new branch, and every task is validated in a sandbox on your repository's own commands.
5. When every check passes, Dualzo opens a pull request. A pull request cannot be opened while validation is failing. Your team reviews and merges it.

Each step is written to an append-only audit log. See the full flow on How it works, or which models and channels are supported on the integrations page.

A checklist for governing AI coding agents

Whatever tooling you use, these are the questions worth answering before agents work on production code:

• Can anyone see what the agent intends to change before it changes it?
• Does a named person approve each piece of work, and is that approval recorded?
• Is the result tested with the same commands your CI uses — and is a failing result blocked, not just flagged?
• Does the agent's code run somewhere without your secrets and without open network access?
• If a change causes a problem next month, can you trace it back to the issue, the plan, and the approval?
• Does a human still review and merge every change?