The safe way to test an AI-generated migration is to give it a disposable, task-scoped database; run the repository’s real migration command against the intended database version; verify both schema and data outcomes; define recovery before testing; and leave a compact evidence record for review. The database credential used by the migration must not reach production, staging, or a shared development instance.
Isolation is more than putting a database in a container. You also need boundaries around credentials, workspace mounts, network access, seed data, migration tooling, and cleanup. Docker Sandboxes are one documented way to isolate an agent, but the validation loop works with a local container, a short-lived managed database, or another controlled environment.
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What the sandbox must prove
A migration test is useful only when it answers the same question a deployment will face: can this repository’s normal migration workflow change a database from the expected starting state to the intended ending state without corrupting data or leaving unsafe artifacts?
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- Target: a new database or isolated database instance created for one task or validation run.
- Starting state: the same database major version and required extensions that the migration is intended to support.
- Execution: the repository’s documented migration command, not a hand-copied SQL fragment.
- Assertions: a before-and-after schema comparison plus checks against rows that could expose a data failure.
- Recovery: a known response to a failed migration, including engine-specific cleanup.
- Proof: a concise record that a reviewer can inspect without receiving secrets or a full data dump.
Draw the trust boundaries first
Separate the database lifecycle from migration access
The authority that creates and deletes a database should not be the same credential passed to the migration process. A provisioning service or operator can create the task database, create a short-lived login, set its expiration, and remove both objects after validation. The migration runner receives only the task-scoped login and connection details required for that database.
Never reuse an ordinary development, staging, or production credential. A generated migration can contain destructive statements, unexpected extensions, or code that behaves differently from its description. If its login can connect elsewhere, a test failure becomes an incident rather than an isolated result.
Isolate the agent’s surrounding environment
Review every path by which the agent can affect something outside the test database:
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- Workspace: a direct read-write mount lets commands modify the host checkout. A clone mode can prevent writes to the host repository while still exposing repository contents for reading.
- Network: restrict outbound connections to the package registries, database endpoint, and other services genuinely required by the task. Block production and staging address ranges.
- Credentials: inject short-lived secrets through a credential proxy or environment mechanism that does not write them to source control, logs, or reviewer notes.
- Tools outside the sandbox: inspect CI jobs, local daemons, mounted sockets, cloud CLIs, and scripts that might retain access after the migration process exits.
Docker’s local sandbox documentation describes each agent running in a microVM with its own kernel, policy-routed outbound TCP, and a Docker Engine separate from the host’s. Those boundaries are useful examples, not a universal requirement. An explicit mount, forwarded port, or host credential can still pierce the intended isolation, so review the effective configuration rather than relying on the product name.
Provision a disposable database
Start from a known, compatible version
Create a fresh target for each migration task or validation run. Pin the database major version and required extensions to the environment the migration is meant to support. A migration that passes on one major version but fails on the deployment version has not been validated.
A shared development database hides important facts. It may contain manual schema edits, old extensions, rows left by another developer, or constraints that do not exist in a clean installation. Those differences make a pass or failure difficult to attribute.
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Give the target a short lifetime
Record who owns the database, when it was created, and when it must be deleted. One vendor-authored migration workflow suggests a 60–240 minute task lifetime for pull-request preparation; that is operational guidance, not a universal standard. Choose a timeout that covers migration and review while ensuring abandoned tasks cannot accumulate indefinitely.
Deletion should be automatic where possible. If an expiration job is the fallback, record its identifier and expected deletion time in the evidence record. A successful test is not complete while an untracked database and login remain available.
Keep sensitive data out of the fixture
A disposable destination limits where writes land; it does not make copied production data safe. Prefer synthetic rows designed around the migration’s edge cases. If real values are essential, use a reduced, masked, approved clone with the same access controls and retention policy.
Use the repository’s real migration command
Preserve the exact invocation
Run the command documented by the repository for applying migrations. Keep the executable, arguments, working directory, and relevant environment settings as evidence. The point is to exercise the same migration runner, ordering rules, lock behavior, and transaction handling used by the team.
Do not replace that workflow with ad hoc SQL. Manually running the generated statement can demonstrate that one statement succeeds while missing discovery of migration files, framework bookkeeping, seed hooks, or transaction wrappers.
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Check transaction compatibility
Confirm how the migration framework wraps each migration before testing operations with special transaction requirements. PostgreSQL’s CREATE INDEX CONCURRENTLY, for example, permits normal writes while the index is built but cannot run inside a transaction block. It also requires additional processing and can leave an invalid index after a failure.
If the repository wraps every migration in one transaction, the migration may need the framework’s non-transactional mode or a separate migration step. Do not “fix” the test by executing the index statement manually; make the repository’s actual migration path represent the deployment behavior.
Design fixtures around migration risks
Read the generated migration and identify the assumptions it makes about existing rows. Seed the smallest synthetic dataset that can disprove those assumptions. Keep output bounded to counts and targeted results instead of dumping entire tables.
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| Migration change | Fixture to seed before applying it | Assertion after applying it |
|---|---|---|
| Add a required column | Rows containing nulls or values that need a default | Every affected row has a valid value and the required constraint is present |
| Add a unique constraint or index | Duplicate values, including duplicates differing only in case or whitespace when the application treats them as equal | Duplicates are resolved according to an explicit rule and the uniqueness enforcement exists |
| Change an enum or status | Each old status, including obsolete or rarely used values | Values are mapped correctly and application queries can use the new representation |
| Add or tighten a foreign key | Valid parent-child rows and an orphan candidate | Valid relationships remain and the intended orphan handling is recorded |
| Rewrite data | Short, long, empty, malformed, and representative values | The transformed values match the rule and row counts remain consistent |
| Change soft-delete behavior | Active, soft-deleted, and boundary-date rows | Queries and indexes treat each state as intended |
Include enough rows to exercise batching and repeated values, but avoid copying a whole dataset when a few deterministic fixtures can expose the failure. Give each fixture a stable identifier so a reviewer can connect an assertion to a specific case.
Capture schema and data evidence
Take a before snapshot
Before running the migration, record a structured snapshot of the relevant schema. At minimum, inspect the tables and columns the migration touches, indexes, constraints, extensions, and views. Record the database engine and major version alongside the snapshot.
Also record the fixture counts and the targeted values that matter to the change. A count of rows with nulls, duplicates, or each old status is often more useful than a complete table export.
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Run the migration and preserve outcomes
- Connect using the task-scoped migration login.
- Run the repository’s documented migration command without editing the generated file for the test.
- Capture the process exit code, duration, and migration runner output after removing secrets.
- If the command fails, retain the database error code and the statement or migration step identified by the runner.
An exit code of zero is only one signal. A migration can report success while leaving an unintended index, constraint, view definition, or data conversion behind.
Take an after snapshot and assert behavior
Repeat the schema inspection and produce a focused diff. Check that intended objects exist with the expected definitions, that obsolete objects were removed only when planned, and that extensions and views are unchanged or deliberately updated.
Pair structural checks with data assertions:
- Compare row counts for affected tables.
- Query the fixture identifiers and verify each transformation.
- Check for remaining nulls, duplicates, orphaned foreign keys, invalid enum values, or malformed rewritten data.
- Confirm indexes and constraints are valid and usable.
- Run representative reads and writes if the migration changes query paths or permissions.
Return only targeted rows, counts, and assertion results in logs. Large outputs increase exposure risk and make review harder.
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Grant the migration login only the privileges needed on the task database, then revoke or expire the login when the task ends. The exact grant set depends on the engine, framework, and objects being changed; derive it from the migration rather than granting a blanket administrator role.
Row-level security is not a replacement for an isolated database or role. PostgreSQL documents that table owners normally bypass row security, subject to specific exceptions and configuration behavior. If row policies are part of the boundary you are testing, use an appropriate non-owner application or migration role and verify the policy with assertions. A test run as the table owner can produce a misleadingly permissive result.
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Classify the expected response
For each migration, document whether recovery means an exercised down migration, a forward fix, restoring the disposable database, or manual cleanup. Do not claim rollback safety merely because a down file exists; rollback must be tested if it is part of the operating procedure.
A failed test database is disposable, but the failure still needs explanation. Save the error, identify the failed step, and state whether the database was deleted, repaired for another attempt, or retained for investigation with an expiration time.
Account for concurrent index failures
With PostgreSQL CREATE INDEX CONCURRENTLY, a failed build can leave an invalid index artifact. Inspect the catalog after failure, follow the documented drop-and-retry procedure, and verify that no invalid index remains in the final snapshot. Because the command cannot run inside a transaction block, the migration framework’s transaction setting must be part of the test plan.
Clean up every resource
- Delete the task database or mark its tracked expiration.
- Revoke or expire the migration login.
- Remove temporary network rules, forwarded ports, and mounted credentials.
- Delete generated dumps, logs, and fixture files that contain sensitive values.
- Record cleanup success or the precise pending action.
Produce reviewer-ready proof
A reviewer should be able to decide whether the migration is safe without opening a live connection or reading a full database dump. Attach a compact record containing:
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- Database engine, major version, required extensions, and target identifier.
- The exact migration command and relevant non-secret configuration.
- Before-and-after schema summaries or a structured diff.
- Fixture cases, row counts, and assertion outcomes.
- Exit code, timing, and sanitized failure details when applicable.
- Recovery action taken, including invalid-index cleanup where relevant.
- Database and credential cleanup result, or the tracked expiration time.
Never include a live connection URL, password, token, private key, or unrestricted table dump. The record should contain enough detail to reproduce the validation with a newly provisioned target, not enough information to access the old one.
Choose an implementation by its boundaries
There is no single required sandbox product. Evaluate a local test container, a task-scoped managed database, or an agent-sandbox database tool against the same engineering questions:
- Isolation: can the migration reach production, staging, or another developer’s database?
- Fidelity: can you pin the intended database major version and extensions?
- Reproducibility: can the task create deterministic fixtures and repeat the run?
- Credential ownership: is provisioning authority separate from the migration login?
- Evidence: can the system capture schema diffs, targeted assertions, and failure details?
- Agent exposure: what do workspace mounts, network egress, forwarded ports, and credential proxies expose?
- Lifecycle: are expiration, deletion, and cleanup observable and automatic enough for abandoned tasks?
- Cost and capacity: can you run the expected number of parallel tasks without weakening the isolation boundary?
Docker Sandboxes document a particularly explicit agent boundary—microVM isolation, a separate Docker Engine, policy-routed outbound TCP, workspace modes, and credential proxying—but those controls still depend on how mounts and forwarding are configured. A simpler local container can be adequate when its database, credentials, network, and cleanup are equivalently controlled.
A practical validation sequence
- Read the migration: list affected objects, data assumptions, required extensions, and operations with transaction restrictions.
- Provision the target: create a fresh database at the intended version, apply only approved extensions, set an expiration, and create a task-scoped login.
- Prepare fixtures: insert synthetic rows for nulls, duplicates, old statuses, parent-child relationships, soft deletes, and representative rewrites as applicable.
- Snapshot the starting point: capture relevant schema metadata and fixture counts.
- Run the real workflow: execute the repository’s normal migration command with secrets supplied out of band.
- Validate structure: compare schema metadata, indexes, constraints, extensions, and views before and after.
- Validate behavior: run targeted assertions against fixture identifiers, counts, and representative reads or writes.
- Resolve failure: follow the predefined recovery path; inspect special artifacts such as invalid concurrent indexes.
- Publish proof: attach sanitized command details, diffs, assertions, errors, and cleanup status.
- Destroy access: delete the database and revoke the login, or verify the tracked expiration job.
This sequence turns an AI-generated migration from an untrusted file into a testable change with a bounded blast radius and reviewable evidence.
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