A review checklist for AI coding agent output (7 things I check before trusting a change)

Description and use

Implementing a checklist and isolated workspaces with diffs reduces code review bottlenecks, prevents accidental changes, and improves auditability of AI agent outputs.

Problem

Reviewing AI coding agent output is a bottleneck; agents produce large outputs that are hard to verify.

Outcome

Implementing a structured checklist and using disposable workspaces with diffs reduces review bottlenecks, prevents accidental changes, and improves auditability of AI agent outputs.

Workflow

• 1. Request a diff instead of a prose summary.
• 2. Verify the agent’s working directory is explicit.
• 3. Ensure the change is isolated to a branch or worktree that can be checked out.
• 4. Review the commands executed and any network activity.
• 5. Confirm credentials are scoped, short‑lived, and appropriate.
• 6. Reproduce the run with the same image, runtime, and starting commit.
• 7. Identify the stopping condition (time, token, approval, completion).

Evidence

Once you write the checklist down, the requirements fall out almost mechanically: sessions need an explicit working directory and state, workspaces need to be disposable and isolated, Git review needs to be native, not bolted on, automations that repeat a routine need their own audit trail, and the whole thing has to run where the code runs, or the credential question answers itself badly.

Source