You built the automation, tested it, watched it run a few times, and moved on.

Three weeks later, you find out it has been failing since Tuesday. Nobody noticed until a customer asked why their invoice never arrived.

That's the problem with automation. Something can work perfectly when you set it up and still stop working later.

I learned this the annoying way with a Zapier flow that moved form submissions into a spreadsheet. It worked fine for about two months. Then the form was updated, and one of the fields started coming through differently. The automation kept showing successful runs, but some of the data was no longer being added correctly.

Nobody noticed for eleven days.

After that, I stopped treating monitoring as something to add later. If an automation is important enough to depend on, I want to know when it stops working properly.

The good news is that you don't need a complicated monitoring system.

Here's how to keep an eye on your automations without turning it into a full-time job.

Why automations fail quietly

Automations don't always stop with a big error message.

Sometimes something changes in a service your automation depends on. An API might start sending data in a different format, and your automation keeps running but saves some fields as blank.

A login session or token can expire, and the automation may stop running until you reconnect it.

A form, spreadsheet, or other tool can also be updated, and suddenly the data your automation expects is no longer the same.

Sometimes you'll get a clear error and know something is wrong.

Other times, everything looks normal.

The automation runs. The platform says it was successful. But the result isn't what you expected.

That's why monitoring shouldn't only ask:

"Did the automation run?"

It should also ask:

"Did it do what it was supposed to do?"

Start with run logs, not alerts

Before setting up lots of alerts, make sure you can see what happened when your automation ran.

Most automation tools, including Zapier, Make, n8n, and Power Automate, keep a history of your runs. You can usually see which runs worked, which failed, and which were skipped.

Check these logs from time to time, especially when you first set up an automation. They can help you spot problems that might not be obvious at first.

For example, you might notice that the same step keeps failing, or that failures only happen with certain types of data.

If you're using a simple script that doesn't keep logs, you can add basic logging yourself.

It could be as simple as saving the time, result, and error message to a file or Google Sheet each time the script runs.

It doesn't need to be fancy.

You just need enough information to know what happened when something goes wrong:

Set up alerts for important failures

Logs are useful when you want to look back and see what happened.

Alerts are useful when you need to know about a problem right away.

You shouldn't have to open your automation tool every day just to check whether an important workflow has stopped working.

Most platforms can send you a notification when something fails. Use alerts for automations where a failure actually matters, such as payment processing, customer emails, orders, or important data updates.

For smaller automations that don't affect anyone directly, you probably don't need an alert every time something goes wrong. A regular check might be enough.

A useful failure alert should tell you:

  • What broke: which automation and which step
  • Why it broke: the actual error message, if there is one
  • When it happened: so you know how long the problem has been there

An alert that only says "Automation X failed" doesn't give you much to work with.

A good alert should give you enough information to know what happened and where to start fixing it.

Watch for silent failures, not just errors

A silent failure happens when an automation finishes without showing an error, but the result is wrong.

Maybe an API returned no data.

Maybe your automation was supposed to send 50 emails but sent zero.

Maybe a field that normally contains data suddenly came back blank.

The automation tool might still say everything worked.

But the result tells a different story.

This is where result checks can help.

Let's say your automation normally updates between 20 and 40 records every day. You could add a check that alerts you if it updates zero records.

You could also flag an unusually high number. If it suddenly updates 500 records, that's worth checking too.

Neither result is normal, even though the automation itself didn't show an error.

Don't just check whether the automation ran. Check whether the result makes sense.

For important automations, you can add a small check at the end of the workflow. Compare the result with what you normally expect and send an alert when something looks unusual.

Use a heartbeat check for automations that should run regularly

A failed run is one thing. But what if the automation doesn't run at all?

If a workflow is supposed to run every hour, every day, or every week, a broken trigger can be harder to notice because there may be nothing in the run history.

The automation didn't fail.

It simply never ran.

This is where a heartbeat check can help.

Services such as Healthchecks.io and Cronitor can wait for a signal from your automation. If the signal doesn't arrive within the expected time, they send you an alert.

For example, if an automation should run every hour but hasn't sent a signal for three hours, you know something needs to be checked.

You can also build something similar yourself. I put together a Python uptime monitor that runs on GitHub Actions that can send alerts to Discord, Slack, or Telegram.

You probably don't need this for every small automation. But for scheduled workflows that you depend on, a heartbeat can catch a problem that normal error alerts can't.

Build a simple monitoring dashboard for important automations

You don't need a dashboard for every automation.

But once you have several important workflows running in different places, it can become hard to keep track of them all.

Instead of checking each tool separately, you can put the important information in one place.

It could be as simple as a spreadsheet showing:

  • Automation name
  • Last successful run
  • Current status
  • Expected schedule
  • Recent failures

You could also use Notion or Airtable and have your automations update the information themselves.

The goal is simply to have one place where you can quickly see if your automations are still running.

The takeaway

Monitoring isn't about assuming your automations will fail.

It's about not having to find out they failed from a customer.

Run logs show you what happened.

Alerts tell you when something breaks.

Result checks help you catch problems that don't show up as errors.

Heartbeat checks tell you when something that should have run never ran.

You don't need to set up everything at once.

Start with the basics. Make sure you have run logs, then add an alert to the automation you care about most. If you run into problems that those don't catch, add result checks or heartbeat monitoring.

The goal isn't to build a perfect monitoring system.

It's simply to know that something is broken before someone else has to tell you.