
Batch vs event-driven AI workflows: which trigger fits?
Batch processing answers a question about a selected set of records. Event-driven processing responds to changes as they arrive. The choice affects latency, duplicates, cost, and what happens to records that already existed before the workflow started.
Decide what historical data should do
A nightly expense summary is naturally a batch. Classifying a newly created request may fit an event trigger. But enabling a change subscription does not necessarily process all existing rows. Treat initial backfill as a separate decision, and state whether updates and deletions should rerun the interpretation or only adjust a report.
- 1Specify whether the unit of work is…Specify whether the unit of work is a snapshot or a change event.
- 2Choose a policy for pre-existing recordsChoose a policy for pre-existing records.
- 3Use stable source IDs and event IDs…Use stable source IDs and event IDs when handling repeat deliveries.
Try it on a small example
- Specify whether the unit of work is a snapshot or a change event.
- Choose a policy for pre-existing records.
- Use stable source IDs and event IDs when handling repeat deliveries.
What to verify
Test the boundary between backfill and live changes so a record arriving during setup is neither lost nor processed twice. Baleybots supports scheduled and event-triggered pipeline paths, with behavior depending on the source. Inspect the specific source contract rather than assuming all connectors use the same trigger semantics. A workflow can combine a controlled backfill with ongoing events, provided the overlap policy is explicit.
Product details checked against the Pipeline architecture on October 4, 2026. These guides describe documented behavior; availability depends on your account and the deployed service. Baleybots is in invite-only beta.