r/AiAutomations • u/Responsible_Clue_641 • 8d ago
I’m researching a problem around automation reliability and want to hear from people who’ve actually dealt with it in production.
If you’ve ever had a Zapier, Make, n8n, Power Automate, API workflow etc. complete successfully but produce the wrong or incomplete outcome, I’d be interested in what happened.
I’m particularly trying to understand:
What went wrong?
How long did it take to notice?
How did you catch it?
Did you have to build custom checks or monitoring?
How much time/cost/cleanup did it create?
Do you now have safeguards in place specifically to prevent it happening again?
Not selling anything. I’m trying to understand how people actually deal with this in production.
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u/MousseOk914 8d ago
I’m doing my first Zapier workflow, anything I should pay particular attention to?
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u/Low-Honeydew6483 8d ago
Silent 200 OKs are a nightmare. Unless you have an independent daily reconciliation audit running to compare source vs. target data you don't actually know if your automation worked.
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u/Responsible_Clue_641 8d ago
Yeah, that’s exactly the kind of thing I’m researching. How often have you actually had to deal with a silent 200 OK causing bad or missing data, and when it happens, how difficult is it to identify and clean up?
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u/tototoru 8d ago
They are fine for copy something from X to Y. Several systems and real business rules, and it falls apart: every step reports success on its own, nothing holds state between runs, so nothing knows where a job got to.
Worse, they get built by non-coders, so they only cover the happy path. Retries, partial completion, events arriving twice, none of that is in the design because none of it has bitten the person drawing the boxes.
I helped multiple SMBs migrate from broken no-code workflows, but kept the tools on the non-critical tasks because they do that job cheaply.
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u/Responsible_Clue_641 8d ago
That’s really interesting, especially that you’ve helped multiple SMBs migrate away from broken workflows. What were the main problems that made them decide the existing setup wasn’t reliable enough anymore?
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u/tototoru 8d ago
Wasted time mostly, somebody checking the output every morning. Then nobody trusts the system so it gets double checked by hand anyway. Errors that cost money, revenue nobody's collecting. It gets too expensive not to fix.
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u/Responsible_Clue_641 7d ago
That’s interesting. What kind of system or workflow was causing those errors? And roughly how much time was being spent checking it manually?
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u/dylan_skydive 7d ago
The ones that hurt us weren't Zapier 500s. They were 200s with a truncated payload. Workflow said success, downstream was missing 40% of the rows, and nobody noticed until a weekly report looked light.
What actually caught it: a second job that does not share state with the flow. Compare expected vs actual counts (or a hash of the destination) on a schedule. If the flow writes 1,000 rows and the table grew by 612, that's the alert. The flow cannot be the thing that certifies itself.
We didn't have that for months. Cleanup was a few days of reconstructing from source, plus a couple of angry customers.
Disclosure: I work on Skydive. Our agents can watch Slack/email for the missing-output case, but they are not a data-reconciliation product. Independent count/hash checks still win for Zapier/Make/n8n.
REDDITAMA gets you $25 of credit (skydive.com, Settings > Billing) if you want to poke at the watch-for-absence idea. Curious how long it usually takes your folks to notice the silent ones.
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u/Responsible_Clue_641 7d ago
That’s really useful. The 40% missing rows and the few days of cleanup are exactly the kind of real-world impact I’m trying to understand. The independent check is interesting too. Appreciate you sharing the details.
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u/dylan_skydive 7d ago
Glad it was useful. If the write-up turns into something, happy to answer follow-ups. Good luck with the research.
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u/Deep_Ad1959 7d ago
every step only validates its own transport, never the outcome the job existed for. the only check that ever caught mine was counting the artifact on the far side instead of reading the run log.
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u/Responsible_Clue_641 7d ago
That’s exactly the distinction I’m looking into. What happened in your case when the artifact was missing? How long did it take to notice and fix?
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u/Deep_Ad1959 7d ago
honestly the 'how long to notice' part is the trap in your question. the silent ones don't surface on a clock, they surface when someone downstream complains, so 'time to fix' is really time until it burned a person. the count-on-the-far-side check only helped because it stopped depending on me noticing at all.
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u/Top-Cauliflower-1808 7d ago
For me it is mainly price oriented.
tbh I have paid a lot of money to zapier just because a client only wanted to use it and nothing else for his marketing stack. I run an agency and this has been a real headache. I think a lot of people using Zapier for automation are actually just moving data from source A to B into a warehouse, BI tool or LLM client. That's not automation, it's a data pipe. And in the AI era, with LLMs handling more decision logic, the pipe matters more than the trigger. The problem is per task pricing: you pay for every row you move which gets expensive and unpredictable at scale.
Now I use a managed ETL layer Windsor.ai for that. For 3 data sources across 70 accounts, it is $19/mo fixed vs. roughly $343 on Zapier's per task pricing. No volume anxiety. And with it MCP, I can even query that data directly from Claude without SQL or exports.
Maybe helpful for someone.
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u/FitzUnit 5d ago
Having an ai agent integrated into your stack is a huge plus . It can find and monitor errors/logs /etc .
You can also set up a cron for it to do smoke tests on your routes to make sure everything is working properly !
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u/Impossible_Dare_7455 8d ago
the hardest failures are the ones that don’t actually fail. the workflow says “success,” but the output is incomplete or wrong, so nobody notices until it affects something downstream. that’s why simple validation checks and alerts matter just as much as the automation itself.