Automation Health Baseline
Turn Automation Studio run metadata into a small health model based on status, runtime and repeated failures.
Evidence tier describes my hands-on relationship with the content. Official sources describe platform contracts/documentation. Production observations should not automatically be treated as official limits.
A repeatable health dataset that catches errors, skipped runs and runtime regressions before they become normal.
Identify the automations that are operationally important.
Decide what “normal runtime” means for each critical flow.
Get latest run status
Resolve the most recent execution per automation.
Inspect recent failures
Keep error details and AutomationInstanceID available for drill-down.
Inspect activity-level health
Find which activity failed or started consuming abnormal runtime.
Track runtime regression
Green is not enough; a flow that became 5x slower is already telling you something.
Investigate Skipped explicitly
Scheduling overlap can be a health smell rather than a harmless status.
Turn the Recipe into checkable work.
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