Preview environments as agent sandboxes

AI coding agents have transformed the economics of pull requests, generating numerous PRs daily without executing code on real infrastructure, posing challenges for verifying runtime behavior. Preview environments, which offer full-stack isolation per PR, are ideal for addressing this by providing a running copy of the application stack to catch integration-level failures such as broken migrations, incorrect environment variables, and misconfigured build commands. These environments consist of an isolated application instance, database, and scoped environment variables, ensuring changes are tested as they would be in production. Cost management is crucial, with strategies including downsizing preview resources, setting expiry policies, and limiting instance counts to prevent financial strain from high-volume agent PRs. The review process shifts focus from code correctness to system behavior, requiring infrastructure-focused checks in a live preview. The lifecycle of a PR involves stages from opening to automatic teardown, with common mistakes like incorrect database scoping and lack of expiry policies potentially leading to operational issues. Isolation in preview environments acts as the trust mechanism, ensuring agent-generated code is safe to merge after successful deployment, migration, and health checks.

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