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Step data explains workflow behavior

If you want to understand why a workflow produced a particular result, step data is often the fastest path. By reviewing what each step received and produced, you can see whether the workflow logic is behaving the way you think it is.

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What step data is useful for

Reviewing step data helps when you need to:
  • understand what the trigger actually provided
  • confirm a step output before the next step uses it
  • find where the workflow data shape changed unexpectedly
  • debug AI or integration results
This is useful in both test runs and live runs.

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Look at the handoff between steps

The most important moment is often not the step itself, but the handoff from one step to the next. Ask:
  • did this step produce what I expected
  • did the next step receive the value I intended
  • is the output too large, too small, or in the wrong format
Many workflow issues come from those handoffs rather than from the step definitions in isolation.

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Use step data to improve workflow design

Step review is not just for debugging failures. It is also useful for improving:
  • field mappings
  • branching decisions
  • AI prompt design
  • output formatting
If a workflow works but feels fragile, step data usually shows you why.

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A good review habit

Whenever you add a new important step, inspect its output during testing before you trust downstream behavior. That small habit prevents a lot of confusion later.