Sandbox verification
How do you verify AI-generated code before release?
Direct answer. Check the change against the original request, architecture, service contracts, data behavior, security boundaries, and rollout plan. Run affected services in a representative sandbox and return inspectable evidence instead of relying on a local unit test or an agent summary.
Verification starts with the requested outcome
A passing test suite does not prove that the right behavior was built. Acceptance criteria should map to the affected product flows, repositories, contracts, migrations, and operational risks before implementation begins.
The verification environment should include the services, seed data, configuration, and dependencies required to reproduce the changed behavior. Missing environment context must be reported as a gap, not treated as a passing result.
Evidence reviewers should receive
- Relevant unit, integration, contract, migration, and end-to-end test results mapped to acceptance criteria.
- Logs and traces showing the expected runtime path without hidden failures.
- Screenshots or captured flows for user-facing behavior.
- Code review findings tied to architecture, ownership, policy, and repository conventions.
- Rollout order, monitors, known risks, and a rollback path for production changes.
How Prinevo helps
Prinevo gives QA and Code Review agents the request, approved context, affected services, and verification plan. They run the change in a sandbox and attach tests, logs, screenshots, contracts, review notes, and rollout evidence to one reviewer-ready delivery record.
Review proof, not promises.
See a sandbox-validated delivery run in Prinevo.