A practical dry-run checklist for AI workflows
A dry run is useful only if it reveals the behavior you will get in production. A model preview can validate wording while missing the destination write, approval pause, or error handling that actually matters.
Test the complete path on a small fixture
Use a fixture with a normal record, an ambiguous record, an empty required field, and a duplicate identifier. Send results to a test destination or an export that you can inspect. Keep the fixture's expected outcomes written down before the run. Otherwise it is easy to redefine success after seeing plausible model output.
- 1Confirm source filters and the number of…Confirm source filters and the number of records selected.
- 2Inspect each output field and its original…Inspect each output field and its original input.
- 3Verify error, approval, cancellation, and duplicate behaviorVerify error, approval, cancellation, and duplicate behavior.
Try it on a small example
- Confirm source filters and the number of records selected.
- Inspect each output field and its original input.
- Verify error, approval, cancellation, and duplicate behavior.
What to verify
Do not infer readiness from one successful record. Record the configuration revision, model, and fixture so you can repeat the comparison after an edit. In Baleybots each run freezes its configuration; use the run's snapshot when investigating what happened. A dry run should end with an explicit decision about which behaviors passed and which remain unverified, especially any writes to real accounts.
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.