What to see on an AI agent monitoring dashboard
Quick answer
An AI agent monitoring dashboard needs five signals, a way to open any one of them, and a short review routine. In Omni Studio, the signals live in the approval queue, the decision log, and the step list. Skip any signal nobody acts on.
An AI agent monitoring dashboard is a short list of signals that shows whether the work is running, whether it's producing something useful, and where a person needs to step in. In Omni Studio, those signals live in the approval queue, the activity log, and the step list on each AI employee’s own computer.
You don't need a wall of charts. You need a few signals, a way to open any one to see what happened, and a routine for looking at them. We set this up for you.
What are the five signals on an AI agent monitoring dashboard?
Each signal answers one question and points to one action. If a signal doesn't lead to an action, drop it.
| Signal | The question it answers | What to do when it moves |
|---|---|---|
| Waiting for approval | What is stuck on me? | Clear the oldest first. If the pile keeps growing, change what needs your yes or who handles it. |
| Edited or rejected drafts | Where is it wrong? | Look for a pattern. If the same fix appears twice, change the instructions or the limits. |
| Handed to a person | What did it leave for me? | Read a few. Each is either a missing rule or a case that should stay with a person. |
| Steps that did not finish | What stopped, and where? | Open the step list, see where it stopped, then retry or take over. |
| Records that are out of date | Is it working from old information? | Check when the calendar, price list or customer list was last updated. |
How do you open a signal to see the run?
A number you can't open is a guess. For any item, you should be able to see what started it, what the AI employee read, what it proposed, who approved or changed it, and what happened in the end. In Omni Studio, every decision is logged, and the step list shows what the AI employee did.
Here's one flag from the handed-to-you list. AI employee Sam, who handles compliance and checks, flags a date that needs a person and attaches the reason.
Check two kinds of health. Technical health asks whether the work ran. Business health asks whether it produced a useful result. A job can run on schedule and still leave an incomplete record or a weak follow-up. So check the outcome, such as the booking that was confirmed or the invoice that was paid, as well as the activity.
What review routine will you keep?
- Each dayClear the approval queue and skim what was handed to you
- Each weekRead the edits and rejections and change one rule
- Each monthDecide what to widen, what to tighten and what to stop
Name one person who owns the routine, and give them the authority to change the instructions as well as read the report. A review that belongs to everyone belongs to no one. A monitoring view nobody reads is worse than none, because it looks like oversight.
How do you test the view and trim it?
Before you rely on any monitoring view, break something on purpose, using sample data. Try a record with a missing field, a request that should go to a person, and an out-of-date calendar. Check that the view shows each one. Then remove anything nobody acts on.
Leave off numbers meant for engineers, such as token counts and model versions, and any activity count with no outcome attached. A sentiment score alone doesn't measure quality either. If you're choosing a tool, ours or anyone’s, ask to see a failed run as well as a healthy one. For the bigger question, read whether AI employees are reliable enough for business operations.
Note. We set up viewing of an AI employee’s own computer for you, and screens may change. The approval queue and the decision log are the parts to start with.
You may also see this called AI agent observability or an AI oversight dashboard. The idea is the same: a few signals, a way to open each one, and a person who owns the review.
What to do next
Set up the first routine around one job. Read what an approval card should show so each item in the queue is quick to judge. Read what it means that an AI employee has its own computer to see the step list. The weekly owner reports use case shows the weekly view, and how approval gates work explains where the queue comes from.
The signals above come from the AI employees in the Omni Studio AI employee library.
Frequently asked questions
What is an AI agent monitoring dashboard?
It's a short view of the signals that tell an owner whether AI employees are on track: what waits for approval, what was edited or rejected, what was handed to a person, what didn't finish, and what's out of date. Each signal opens to the run behind it.
Where do I see what an AI employee did in Omni Studio?
In the approval queue, the decision log and the step list on the AI employee's own computer, which we set up for you. Every decision is logged with who approved it, and an approved action runs once.
How often should I review it?
Each day, clear the approval queue and skim what was handed to you. Each week, read the edits and rejections and change one rule. Each month, decide what to widen, what to tighten and what to stop.
Which numbers should I leave off?
Leave off numbers meant for engineers, such as token counts and model versions, and any activity count with no outcome attached. A sentiment score alone doesn't measure quality either. Ask any vendor to show a failed run as well as a healthy one.
Is this the same as AI agent observability?
You may also see it called AI agent observability or an AI oversight dashboard. The idea is the same: a few signals, a way to open each one, and a person who owns the review.
Sources
- Omni Studio security and controls, Omni Studio, read Oct 7, 2026.
- Omni Studio weekly owner reports use case, Omni Studio, read Oct 7, 2026.
Omni Studio facts come from omni.studio and the Omni Studio product team.
