A review discipline for AI-written code (Cursor, GitHub Copilot and others)
AI makes it cheap to produce code, so review and testing become the bottleneck and the safeguard. Here is a lightweight discipline that scales.
By the Halden editorial team
The shift
Writing is cheap, understanding is not.
When an agent can produce hundreds of lines in minutes, the risk moves to whether anyone understands them. Teams that thrive treat AI output like a pull request from a new colleague.
A simple checklist
Five questions per change.
Use the same short checklist for every AI-assisted change.
- Do I understand what this does and why?
- Are there tests, and did they run?
- Does it follow our conventions?
- Does it touch security, data or licences?
- Is it small enough to review properly?
Automate the basics
Let tools catch the obvious.
Continuous integration, linters, dependency checks and secret scanning catch many issues before a human looks. Keep them mandatory for AI-assisted changes.
Keep people accountable
A person owns every merge.
The developer who merges is accountable, whoever or whatever wrote the code. Record in your guidelines which tools are approved and how to handle sensitive repositories.
A next step
Turn this into a decision for your organization.
Explore how AI Implementation works with leaders and teams, or start with a short reflection on where you are today.