Yesterday, 07:57 AM
Something I don't see discussed enough: when AI automations fail *silently*, that's way more expensive than when they fail loudly.
A loud failure: Zapier throws an error, the webhook returns 500, Slack pings you. You fix it.
A silent failure: the workflow runs, returns something that *looks* correct, and it's wrong in a way nobody catches for 3 weeks.
Examples I've hit in the wild:
- A lead-scoring automation that started tagging every inbound email as "high priority" after a model update changed its default behavior.
- A summary bot that quietly started dropping bullet points when inputs ran long, because the context window started truncating and there was no check.
- An invoice parser that swapped "quantity" and "unit price" columns for one vendor's new invoice format.
- A draft-email agent whose tone drifted to overly formal after a prompt edit; nobody noticed until a client replied "is everything ok?"
- A classification bot that returned "" for edge cases, and the Make.com filter interpreted "" as "don't process" — so a whole category of tickets stopped being created.
What I now do differently:
- One explicit "sanity check" step after every AI call — either a second model call that validates output, or deterministic checks (required fields present, types correct, values in range).
- Logging that samples ~5% of outputs for human review weekly, not just failures.
- Alerting on "missing" outcomes, not errors. (e.g. "0 emails tagged 'high priority' today" is suspicious and should page you.)
- Goldenset regression tests. 20-50 known inputs with expected outputs; run them automatically when prompts change.
Who else has a good silent-failure horror story? Always helpful to compare notes.
A loud failure: Zapier throws an error, the webhook returns 500, Slack pings you. You fix it.
A silent failure: the workflow runs, returns something that *looks* correct, and it's wrong in a way nobody catches for 3 weeks.
Examples I've hit in the wild:
- A lead-scoring automation that started tagging every inbound email as "high priority" after a model update changed its default behavior.
- A summary bot that quietly started dropping bullet points when inputs ran long, because the context window started truncating and there was no check.
- An invoice parser that swapped "quantity" and "unit price" columns for one vendor's new invoice format.
- A draft-email agent whose tone drifted to overly formal after a prompt edit; nobody noticed until a client replied "is everything ok?"
- A classification bot that returned "" for edge cases, and the Make.com filter interpreted "" as "don't process" — so a whole category of tickets stopped being created.
What I now do differently:
- One explicit "sanity check" step after every AI call — either a second model call that validates output, or deterministic checks (required fields present, types correct, values in range).
- Logging that samples ~5% of outputs for human review weekly, not just failures.
- Alerting on "missing" outcomes, not errors. (e.g. "0 emails tagged 'high priority' today" is suspicious and should page you.)
- Goldenset regression tests. 20-50 known inputs with expected outputs; run them automatically when prompts change.
Who else has a good silent-failure horror story? Always helpful to compare notes.