Automations that don't break

What you'll see in this lesson

1. Why automations break

   Almost no failure is your workflow's fault: an API fails, odd data arrives or the model replies with something unexpected.

2. Retries with increasing waits

   For a transient error, retry waiting twice as long each time, with some randomness, and never beyond a maximum of attempts.

3. Error workflows: no failure goes unnoticed

   When a step really fails, the workflow should take an error path that saves the case and alerts a person.

4. Idempotency: no invoice or email sent twice

   An operation is idempotent if repeating it doesn't change the result: the key is remembering what you've already processed.

5. Put a cap on tokens and cost

   Limit what goes into and out of the model on each execution and work out the cost before the bill tells you.

6. Traces: knowing what happened at each step

   A trace records each step of an execution: what went in, what came out, which tool was called, how long it took and how many tokens it used.

7. Test with sample data before publishing

   No change reaches production without first going through the same test cases, including the odd ones.

8. Alerts: find out before your customer does

   Set up alerts for what matters: repeated failures, a daily cost spike and queues that don't move.

9. Summary

   A robust workflow retries wisely, doesn't duplicate, caps its spending, leaves traces, alerts on patterns and is tested before publishing.