A team runs an order API, a billing job, and a customer page that reads order status from the API. All three depend on how the app stores status in Postgres. When the team asks separate agents to update those parts of the app for a new status field, each change can pass on its own branch and still break the app if it goes live before the database is ready.
Keep one release queue for each production service so agents can work in parallel while reviewers decide what can go live together. For each entry, record the exact code, the database state it needs, and the test that shows it can run alongside the code already serving customers.
Show the dependency between the changes
Say the order app is called Parcel. Its team wants to add status_code while customers still use a release that reads `status). In the queue it shares with agents and reviewers, the team records the commit, image identity where available, required schema state, tests, owner, and plan for switching back.
| Entry | Change | What must happen first |
|---|---|---|
| A | Add nullable status_code | Review and test the schema change |
| B | Write both old and new status fields | Confirm A has finished |
| C | Read the new field as planned | Finish and check the backfill |
A backfill updates existing records so they contain the new value. After B, new orders can get both fields while existing orders still need that update. Entry C can rely on status_code alone once the team verifies that the older orders have usable values too.
The queue can live in a release document that a named reviewer checks, or CI can enforce the order. Record which method the team uses and make sure the reviewer or rule checks that A has finished before allowing B to deploy.
Let old code keep working during the change
Start by adding the new field without removing the old one. Parcel's existing code can continue reading status. The new writer can fill both fields so either version can understand new orders.
Update older records in bounded batches. This limits the work done at once and makes it easier to stop if the change slows normal requests. Record progress so a retry can continue safely rather than repeat the entire task.
Old code may still write only status during the rollout. Keep reading that field while old writers can run, or use another tested way to keep both values correct. Filling the new field once is not enough if an old process can change the old value later.
Plan for new writes before starting the backfill. If the job copies an older status over a fresh update, it can leave status_code wrong even after it has visited every row. Choose a method that protects current writes, then check the resulting values before switching readers.
Only switch readers once the expected records have usable new values. Keep the old field and compatible code until the team no longer needs the previous release. Removing it too soon can make an image rollback fail.
Test the versions that may run together
Test the versions that can run together during a rollout. Old and new app processes may both serve requests, and a job may retry a payload from the previous version after the new code is live.
For Parcel, the team can create an order with the old code and read it with the new code, then try creating an order with the new code and reading it with the old code. After updating the order, check that both versions show the expected status.
Review the exact Postgres command and lock behavior on the database version in use. A schema change that finishes quickly on a tiny fixture may take longer or block work on a real table. Use a safe data shape close to production and define when to stop. PostgreSQL ALTER TABLE reference
Rolling an image back does not undo a database write or recreate a removed column. Keep a separate recovery plan for data changes that would make the previous image unsafe.
Keep test evidence tied to the chosen code
By the time one agent's tests finish, another release may have changed production. Recheck the candidate against the current release state before moving it out of the queue.
Where the workflow supports it, build a candidate once and keep the image's identity with the tests and deployment. If the platform builds at deployment time, record the selected commit and resulting deployment so a green check stays tied to the code it actually tested.
Use tests that cover the changed behavior and a core user flow. For Parcel, that means saving an order, changing its status, and reading it through the paths that need compatibility. Verify the live revision after rollout, then watch errors and response times for a period suited to the app's normal use.
Name the person who can halt the queue. Until the team has a tested reason to allow overlap, run one production rollout at a time for that Service. This makes the current state easier to establish if something fails.
Rehearse a release that must wait
In staging, try submitting B before A. The workflow should hold B and explain which condition is missing. Then apply A, check it, and allow B to proceed.
Next, try switching back to the previous image while the new nullable field remains, and confirm that the old app can still read and write orders. Record what happens in these drills so the team knows which steps the workflow enforces and which depend on a reviewer.
If a person caught the bad order, the control is that review step. If CI refused it, record the rule that did so. Both approaches need an owner and a check after workflow changes.
Keep the agent's credentials limited to the work it needs. Staging diagnosis and production deployment are different grants. AWS recommends least privilege and review of unused access; review connected tools when an agent session gains a new route into the environment. AWS least-privilege guidance
Put the queue around Forte's release triggers
Forte Services can deploy on a push to a chosen branch, on a published GitHub release, or from a commit you select for a manual deploy. Use staging pushes for feedback and put the queue check before the production trigger, so the reviewer or CI rule can stop a change whose dependencies are still pending. Forte Service triggers
Forte's deployment page and GitHub status help trace a commit through its build and rollout. Build features include a versioned artifact and rollback for a failed Service update when an earlier healthy revision exists. Schema and data recovery still need their own plan. Forte deployment workflow, Forte build features
Your team owns migrations, queue rules, test data, and release approval. Build runs are billed, and non-default Service sizes require verified billing. Forte pricing
For Parcel, start with the blocked-release drill on a test Service. Follow the Service trigger guide, choose a production trigger, and make the queue check part of the path that can activate it.