
Use Postgres change events to trigger AI work
A database change can start useful work without repeatedly scanning the entire table. The trigger should express the business event you care about, rather than treating every update as a new AI task.
Prevent the workflow from triggering itself
If an AI workflow reads a request row and writes a classification back to the same table, that write can produce another event. Decide which fields or states represent new work and which are merely the workflow's own output. Keep the source event ID and record ID separate: one row may have several legitimate updates.
- 1Identify the insert, update, or delete events…Identify the insert, update, or delete events that matter.
- 2Filter irrelevant changes before spending model callsFilter irrelevant changes before spending model calls.
- 3Test a destination write against the source…Test a destination write against the source subscription.
Try it on a small example
- Identify the insert, update, or delete events that matter.
- Filter irrelevant changes before spending model calls.
- Test a destination write against the source subscription.
What to verify
Use a staging table to exercise the complete trigger path. Confirm what happens if events arrive out of order or a row is deleted before processing. Baleybots documents a postgres_changes connection that returns installation SQL and a one-shot secret, allowing notifications without a standing database read credential for that connection type. Follow the returned installation contract and retain the secret securely; do not treat trigger creation as proof that delivery has been tested.
Product details checked against the Public API reference on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.