Published by Anvil Coder · Halvic Labs GmbH
Updated
Supervising AI agents: approvals and review
Separate approvals during AI coding from final code acceptance. Understand project settings, verification logs and human review before merging.
Which decisions stay with the team?
Supervising AI coding agents involves several decisions: which task may start, which actions require approval, and whether the final result is acceptable. These should not disappear into a single “automatic” switch.
Anvil Coder configures supervision per project. With no level set, a task proceeds without additional approval waiting points. Check the actual configuration before starting instead of assuming a universal default.
How do you choose appropriate controls?
Use the risk of the change as your guide. A small display bug deserves different attention from a permissions change or data migration. Establish:
- Which files and data may the task affect?
- Which actions need review during implementation?
- Who can answer questions or stop the task?
- Which tests are required before acceptance?
Approvals do not automatically reduce business-logic errors. The approver needs clear information about the specific action and its consequences.
Does approval replace pull request review?
No. An allowed action is not an accepted final result. The hosted service produces a pull request draft; a human merges it into the main branch.
Review requirements, diffs and executed tests together. Commits record changes, not automatically passed checks. Integration failures can remain even with close supervision.
How should supervision evolve?
Start with a bounded task and identify which information was missing at approval points. Adjust rules and responsibilities using those observations. Use guardrails for recurring constraints and the security overview to understand operating-model limits.
