Synthetic user testing: what it is, what it finds, and where it stops
Synthetic user testing means AI agents, acting as simulated users, try to complete tasks in a product so a team can find usability problems without recruiting participants. It is fast and repeatable. It is not a stand-in for hearing from real customers.
Last updated 17 September 2026
What synthetic user testing is
In a usability test, people try to do something with a product while someone watches where they struggle. Synthetic user testing replaces the participants with AI agents. Each agent is given a goal (sign up, find the pricing, check out) and a profile that shapes how it behaves: how patient it is, how comfortable with software, how much it already knows, what device it is on. The agents attempt the task and the test records what happened.
The appeal is speed and repetition. A test that takes days to recruit, schedule and watch can run in minutes, against a staging build, before a release, and again after every change.
Two different things share the name
- Simulated research participants
- An AI answers interview or survey questions as if it were a type of customer. You learn what that customer might say about a concept or a message. Nothing is used: no product is clicked.
- Simulated users of the product
- An AI agent operates the actual interface, in a browser, and tries to get something done. You learn where the interface itself gets in the way: a button that doesn't read as a button, a form error with no explanation, a price that changes at checkout. This is the kind Meerkat does.
The two answer different questions. The first is closer to concept research; the second is closer to a usability test. Our comparison of AI usability testing tools sorts the products on the market by which one they do.
What it is good at finding
- Dead ends and errors: a validation message that doesn't say what is wrong, a disabled button with no reason, a step that loops.
- Things that are hard to find: the sign-up link below the fold, the setting nobody would look for under that name.
- Copy that confuses: jargon a newcomer can't parse, labels that mean something different to someone outside the team.
- Surprises: a price that changes between the listing and checkout, a required field that appears only after submitting.
- Differences between people: the same flow run by a patient expert and an impatient first-timer on a phone, side by side.
- Regressions: a problem that was fixed and came back in a later release, caught by running the same task again.
Where it stops
An AI agent is not your customer. It does not have their context, their stakes or their history with your product, and it can misread a screen in a way no person would. The Nielsen Norman Group advises against using synthetic users as a replacement for research with real people, and we agree.
- Use it to find friction worth investigating, not to predict conversion rates or to decide what customers want.
- Check the evidence. A good tool shows the screenshot behind every claim so you can see whether the problem is real. In our study of 21 websites, about seven in ten problems the personas raised held up; most of the rest came from how the test ran.
- Look for agreement. A problem several independent agents hit is more likely to be real than one only a single run ran into.
- Then talk to people about the problems that matter, with better questions than you had before.
How a run works in Meerkat
- You give a URL, the task in plain words, and which personas should try it. Templates cover sign-up, onboarding, checkout and a first visit.
- Each persona drives a real browser, seeing only the screen, and says before each action what it sees, what it will do and what it expects.
- A second model reviews every step against the screenshots, groups what went wrong into problems, and proposes a fix for each.
- You get a report: the outcome in a sentence, the problems worst first with screenshots and the personas' own words, and a replay of each run.
The details, including what personas can't do (pay, solve CAPTCHAs, receive email codes), are in how Meerkat works.
Try it on your own product
Sign up free and you get 10,000 credits, plus 3,000 more every month. A persona run usually uses about 1,000. Or read an example report first: it is a real, unedited run of Meerkat against its own website.