Monitoring Automations So They Fail Loudly
Catch broken, stalled and drifting automations before customers or finance notice
A taste of a lesson
Our lead capture flow showed no errors for three weeks, but it had stopped adding leads. How could we have caught that?
That is a classic absence failure: nothing ran, so nothing errored. A heartbeat check would have caught it: if no new lead arrives within, say, 24 hours on a weekday, alert the owner. A volume check is the next layer: compare daily leads with the usual range. Then find the cause, which is often an expired connection or a changed form. Finally, reprocess the missed leads from the form's own records. Do you know which account that flow was authenticated with?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Build an automation inventory with owners, credentials and criticality
- Detect silent failures with heartbeat, volume and reconciliation checks
- Sample AI outputs regularly and re-test after model or input changes
- Design alerts and digests that reach owners without flooding them
- Run incident reviews that reprocess missed items and improve checks
Lesson plan
- 1 How automations fail silently Recognise failures that produce no error message at all. Start
- 2 The automation inventory Know every automation, its owner and what it touches. Start
- 3 Heartbeat, volume and reconciliation Detect absences and mismatches, not just errors. Start
- 4 Watching AI steps and costs Keep AI quality and spending under regular review. Start
- 5 Alerts that get acted on Route alerts by severity to owners with clear first actions. Start
- 6 Incidents and offboarding Recover from failures fully and prevent ownership gaps. Start
Try asking
About this tutor
For operations leads and automation owners running many flows across a business. The worst failures are silent: a trigger stops firing, a connection expires, an AI step starts mislabelling after a model change, or volumes drop to zero without an error. This advanced tutor teaches monitoring that catches these: run histories, heartbeat and volume checks, data quality checks, AI output sampling, cost tracking, alert design, an automation inventory with owners, and incident reviews. You will design a monitoring plan for your own portfolio of automations.
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About the teacher
I teach operations teams to build AI automations for documents, requests and data that fail safely
9 tutors 316 lessons taught Sample
I work with operations teams who handle volume: invoices, support tickets, forms, contracts and meeting notes. My background is in finance operations and process improvement, so I think about accuracy, audit trails and what happens at month end when something quietly broke two weeks ago. I teach how to put AI steps inside workflows so that extraction, classification and summaries...
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