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Roll Autter out in stages so contributors understand the feedback and maintainers can calibrate the signal.

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Prepare the rollout

Before inviting the team:
  • choose one active repository
  • identify maintainers who can evaluate review quality
  • collect a small set of standards the team already enforces
  • decide whether developers will also install the Autter CLI
  • review Data and privacy before enabling connected CLI mode

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Roll out the platform

1

Connect one repository

Follow GitHub setup or GitLab setup.
2

Observe default reviews

Let maintainers review several representative pull requests before adding many custom rules.
3

Add established team rules

Start with standards that already appear in human review comments. See Create a custom rule.
4

Explain how to handle feedback

Ask authors to verify findings, review suggested fixes, and escalate uncertain behavior to a human reviewer.
5

Refine noisy checks

Change or remove rules that repeatedly flag valid code.
6

Expand to more repositories

Reuse only the rules that apply across those repositories. Keep repository-specific standards scoped locally.

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Add CLI attribution

Install the Autter CLI on developer machines when the team wants explicit AI authorship in Git history. 
curl -sSL https://autter.dev/install.sh | bash
autter status
Local-only mode requires no account. Connected mode can contribute attribution and prompt history to organization dashboards.
Decide the prompt storage policy before a team rollout. Developers should not place credentials or sensitive customer data in coding-agent prompts.

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Set expectations for contributors

Tell contributors:
  • Autter feedback is review input, not an automatic statement of correctness
  • generated fixes still require tests and human review
  • team rules should reflect documented engineering policy
  • false positives should lead to rule refinement, not silent workarounds
  • AI authorship comes from explicit agent attribution, not AI detection

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Measure the rollout

Use Analytics to inspect system-level trends such as review time, pull request size, and recurring findings. Do not use one metric to rank individual engineers.