Imagine an automation that prepares a summary of orders every Monday and sends it to the team. You connect the sources, choose the information to include, and set the delivery time. The first report arrives, the numbers add up, and nobody has to combine three files by hand. This is exactly what you wanted.
A few weeks later, though, a column might be renamed, access might expire, or a new type of order might appear. The automation may fail with an error. The worse outcome is that it keeps sending a report that looks right but leaves out some of the data.
That kind of problem can be hard to spot. The report keeps arriving, and the team keeps making decisions based on it. That’s why automation needs checks that catch problems before someone runs into them during their work.
Start by checking what actually came out
A “completed” message is useful, but it isn’t enough on its own. For our Monday report, we need to see whether it includes the orders we expect, uses current information, and reaches the people who need it.
Suppose the report still contains last week’s data. The file was created, the message was sent, and the system reported no error. Yet the team has a misleading picture. Checking delivery alone won’t catch that. We also need to know whether the report contains what people expect from it.
For the first check, compare the report against a few specific records in the source system. Then automate some of those checks: look at the last update time, whether each source is available, or an unusual drop in the number of records. A difference isn’t always an error, but you should be able to explain it.
What happens when a source goes down?
During the first test, you usually want to see everything work. It’s worth testing the opposite too: a missing file or a service that doesn’t respond. Do this in a test run so the result doesn’t reach your colleagues as an ordinary report.
If one of three sources is unavailable, does the automation wait or build a report from the other two? If it sends a partial result, the missing information needs to be clear. Otherwise, the team may interpret fewer orders as a real decline when some simply couldn’t be loaded.
Retries deserve the same attention. If sending a message ends without confirmation, that doesn’t necessarily mean it wasn’t sent. Before trying again, the system should be able to establish what it already did. That check matters even more when creating orders or invoices.
Agree on who handles a problem
An error alert helps only if it reaches someone who can act on it. An administrator may restore access, a salesperson may supply missing information, and a team lead may decide whether to send an incomplete report. Each needs different information. “Something went wrong” won’t get them very far.
Start by agreeing on who checks the result, who receives technical problems, and who can change the rules. Keep a short note explaining where the automation runs, where its data comes from, and how to pause it. When something needs fixing, you won’t have to start by tracking down whoever originally set it up.
A useful alert says what failed, what it affects, and what the person should do next.
A small change still needs a quick check
Even a simple task changes over time. The report now needs to arrive on Tuesday, another team joins, or a data source moves. The edit may take a moment, but it’s still worth checking that the running automation actually uses the new settings.
Look directly in the tool that runs it. Are the schedule, instructions, source, and recipients correct? Does it have the access it needs? A note in the documentation or confirmation that the settings were saved doesn’t tell you what the next run will actually do.
For changes that could send messages or write to another system, check the result in a test run first. If there’s a chance of sending or writing something incorrectly while you make changes, pause the automation. Once it’s back on, check the first result. A simple edit may need only a quick look; a new data source calls for checking its contents too.
If no message arrives, is everything fine?
You expect the Monday report every week. If it doesn’t arrive, something is missing. But for a check that only flags overdue orders, silence may be the correct result. You need to distinguish a task that ran and found nothing from one that never ran at all.
That doesn’t mean receiving another message after every check. It can be enough to have a record of when it last ran and what happened. An alert should arrive when something fails, a scheduled run is missed, or the task needs a decision from you.
When an automation sends too many updates, people get used to ignoring them. An important alert can easily disappear among dozens of messages confirming that nothing happened.
Does anyone still use the report?
An automation can work perfectly and no longer help anyone. The team may have switched to a different report, messages may go to people whose roles have changed, or someone may be recalculating the same column by hand every week.
After a while, ask the people using the result: Is this still useful? What’s missing? What do you have to finish manually? Sometimes the answers lead to a small adjustment. Sometimes you discover that you can turn the task off altogether.
How often to review it depends on what it does and the consequences of an error. An internal weekly summary needs different attention from a system that directly changes orders. Agree on who will check the result and when. If you find a recurring error, update the automated check too. Next time, a colleague shouldn’t have to be the one who spots it.
Pick one automation you already rely on
Choose a task you depend on today and work through these questions:
- When did it last run, and where is the actual result?
- How do you know it used correct and complete information?
- What happens when it fails, and who gets notified?
- Could a retry perform the same action twice?
- Who can change the settings or stop the task?
- Does anyone still use the result, and does it help them?
If you hesitate on an answer, start there. You may only need a check for fresh data or a different recipient for error alerts. You don’t have to rebuild the whole system. A small adjustment can be enough to catch a problem early and make sure someone knows what to do about it.
For more on reliable tools and AI agents, see SaaS isn’t dead. What we pay for is changing. If you want to start with one specific business process, take a look at the AI workflow sprint.