With three or five machines you can hold the whole picture in your head. Which one is running what, which one refused to connect yesterday — you remember. At thirty or fifty, that stops being true. A device that was fine yesterday may simply not be online today. A script on one machine parked in front of a dialog, the screen still showing yesterday’s frame, and you have no way of knowing until you open it.
The real trouble is not a broken device. It is not knowing which one broke. That sounds like a pointless thing to say, but anyone who has done this knows exactly what it means. You usually discover the problem when a whole batch of tasks fails together, you go digging for the reason, and only then notice that a few of those machines had dropped offline long ago with the work still queued against them.
So inspection was never about repairing devices. It is about finding out which one is off as early as possible. That is an information problem, not a technical one. Once you see it that way, it becomes clear which parts you can hand to a tool and which parts you would not trust a tool with.
1. Once the Device Count Climbs, the List in Your Head Fails First
There is only one place where every run leaves a receipt: the execution history. Who ran, how far they got, what error came up — read it and the picture mostly lines up. Most of the raw material for inspection is sitting right there.
What changes with scale is the cost of finding out. One device that misbehaves is a nuisance. Eight devices quietly offline for two days is a hole in your schedule that no amount of clever automation gets back.
2. The Inspection Signals Really Come in Three Kinds
The first is status on the Devices page, and it is the one you look at before anything else. Offline devices cannot run tasks — that is a hard rule, and no amount of scripting changes it. So open the Devices page and scan for anything whose status looks wrong, without worrying about the detail of what each one is doing.
The second is whether a task has got stuck, and you cannot see that from online status alone. A device can be perfectly online while the script sits on one step going nowhere. The interface will not raise an alarm on its own, so you go into the execution history and read the state each task ended in.
The third is whether errors are being thrown. The execution history keeps the error from every run, and when you read it the thing to watch is whether the same kind of error keeps recurring. A single failure is very likely a network blip. Several in a row usually means the step logic is wrong or the interface itself has moved on.
Aliases and grouping do more for this than people expect. On the Devices page, sort each phone by purpose and give it a name you can recognize, and the whole job gets faster later. It also feeds straight into how you scale, which what device capacity actually looks like in practice picks up from there.
3. What AI Can Judge, and What Only You Can
The part you can hand over is “run the check action once and write down the result”.
Take a check you do every day: confirming a batch of devices can still open an app, or that a particular page can still be reached. Jobs with fixed actions and a clear pass-or-fail standard can become a workflow on a schedule that runs itself, with the results waiting in the execution history so a failing device stands out immediately. To actually drive the phones, describe the actions on the workstation chat page, or pick devices in a workflow and run it directly — both work.
Do not rush into a workflow on day one. Start on the chat page with one online device and spell the actions out line by line, something like “open the app, wait two seconds, go to the home page”. Run it two or three times until it reproduces reliably, then move it onto the workflow canvas and save it as a template. The benefit is concrete: when it jams halfway, you know which sentence it jammed on, so you have a target to fix.
One prerequisite not to skip — scheduling is local, so the computer has to stay on and so does the workstation.
Now the parts you cannot hand over.
Physical actions are one. When a device drops offline, plugging and unplugging cables, swapping ports, cutting power and reconnecting — that is a person’s job. The software cannot even see the plug.
Decisions about whether to replace a device, or to pause a group, are another. A tool can tell you a machine has failed many times in a row, but whether to swap it, and whether the fault is the device or the task itself, is yours to judge. Devices wear out with use, so those calls have to be made against the actual state of the hardware, and what long-term running does to the phones themselves covers the maintenance rhythm around that.
There is a third kind that hides better: the task design itself has fallen behind. The interface was redesigned, the flow changed, and the task is still running the old steps — finishing every time, with the wrong result. This is invisible in the execution history because no error was ever thrown. Spot-checking a few results by hand now and then beats any automation here.
4. Setting an Inspection Rhythm You Will Actually Keep
I have seen it often enough. Week one, enthusiasm runs high and someone inspects morning, noon and night. Two weeks later it is back to looking only after something breaks. Set the frequency too densely and it is guaranteed not to survive.
My advice is to set it low — low enough that you are certain you can keep it up.
Start with one fixed daily action: two minutes on the Devices page before work begins, scanning for how many devices are offline. This step needs no tooling at all, just your eyes, and it carries the most value because it filters out the devices you are about to use today.
Next, turn the checks that need repeating into a workflow on a schedule, running at a fixed time and dropping results into the execution history. Then you only need to read the record once a day to see whether failures are clustering. How to arrange the schedule is explained at https://www.ieasyrun.com/docs/schedule.
Once a week, do one more thing: sort the recurring errors in the execution history into groups and see whether they share a cause. A messy pile of errors usually collapses into two or three root causes, and one fix clears a batch of them.
As for the extreme case where dozens of devices fail at once, hand it to a human. What that needs is someone watching the screen, not a script running.
5. A Few Easy Ways to Misread the Picture
The first: treating “it finished” as “it finished correctly”. As noted, no error does not mean a right answer, especially for tasks that change data. Think about how you will verify the result before you run it.
The second: assuming online means everything is fine. Online only means the connection held. It says nothing about whether the app crashed or the page changed underneath you. Online status is worth less than it looks.
The third: reading a task problem as a hardware failure. When the same batch of tasks fails across several devices at once, it is usually not broken machines but a changed task or interface. Check the errors in the execution history first, and only then start pulling devices apart.
The fourth: spreading the inspection net too wide. A full sweep across dozens of devices is not cheap, and most of the time it is unnecessary. Pick representative devices per group first, and run a full sweep only on the groups that matter. That is the version that lasts.
Once the fleet is large, most of the hard problems are no longer on any single device — they are in how you organize the whole set. Build the rhythm first, and the cost side of running this many phones is worth reading alongside it in what an Apple cluster control setup actually costs.
Install links and further reading:
- iEasyRun site and install guide: https://www.ieasyrun.com/docs/install
- Devices documentation: https://www.ieasyrun.com/docs/devices
- Execution history documentation: https://www.ieasyrun.com/docs/execution
- Scheduling documentation: https://www.ieasyrun.com/docs/schedule
- iEasyRun documentation: https://www.ieasyrun.com/docs
- Further reading: AI agents driving real devices end to end
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