Method
Synthetic users: how they work and what they are for
Synthetic users are simulated people, played here by AI agents that use your app the way your users would. Used well, they find what is broken and confusing before launch. Used badly, they produce confident fiction. This page covers the difference.
What are synthetic users?
Synthetic users are simulated people with a defined profile, such as a role, an age, a country and a need. In Mimiq an AI agent plays each one: it uses your app in a real browser, acting as that user, and reports where it got stuck. One is an anecdote; several show which problems repeat. They are useful for finding what is broken or confusing, not for predicting how many real people will convert.
Why people look for synthetic users
Teams want to pressure-test a flow before the first real user touches it. Recruiting real participants takes time and money, and asking one chatbot produces one polite opinion. Synthetic users promise many users, quickly. The question is how to get ones worth acting on.
- AI agents matched to your audience, using your app in real browsers instead of describing it.
- Built to stop where a user would, not to be agreeable.
- Each issue comes with the steps and a screenshot, so you can check it.
- Best used as a first pass before real research and live experiments.
How it works
- 01
Describe the users
Paste a link, and Mimiq matches the agents to who the app is for. Describe the users in plain words if the guess is too broad, or build agents from your own customers by importing a CSV.
- 02
Run the agents
Each synthetic user opens your app in a real browser and tries a task in plain words: 5 agents by default, or 1, 3, 5, 10 or 20, and you watch their screens.
- 03
Question the results
Read the ranked issues, then ask a follow-up or interview any one agent about why it stopped.
What a synthetic user is
A synthetic user is a simulated person with a defined profile: who they are, what they need, and where they are coming from. In Mimiq an AI agent plays that person while it uses your live app in a real browser.
One synthetic user is an anecdote. Several are more useful. When agents with different profiles try the same sign-up, the pattern of who gets through, who hesitates, and who gives up tells you more than any single run.
Imported customers become agents too: one agent per row of your CSV, with names, emails and phone numbers stripped before upload and one credit per customer. Either way, they are simulations, not real people and not a recruited panel.
Useful synthetic users stop where a user would
The main failure of naive synthetic users is agreeableness. Language models are trained to be helpful, so a simulated person asked 'would you sign up?' tends to say yes. That produces enthusiasm and no useful findings.
The fix is to give them something to do instead of something to opine on. A Mimiq agent gets a task and a real browser. If it cannot find the button, it stops, and the report shows the step and the screenshot. Its reasons are ranked by how many agents raised them.
A second failure is sameness. If every simulated user behaves the same, you learn one thing over and over. Varied profiles are what show where different kinds of users get stuck in different places.
Where synthetic users help most
They work best early: before the first real user, before a release, and on every preview. They are good at finding problems that stop whoever tries: an error on screen, a button that does nothing, a form that rejects good input, a dead end, wording that sends people the wrong way.
They can run on every change. A GitHub Action runs the agents on each preview deploy, and your coding agent can run them through the MCP server before you review the diff.
They make iteration cheap. Send the same agents through the next version and see who gets further. That is hard to do with recruited participants on a tight schedule.
Where synthetic users fall short
Synthetic users are weakest where the answer depends on knowledge or experience that is thin in public text: niche expert buyers, emotional and lived experiences, local context, and genuinely new product categories.
They also cannot tell you what your existing customers actually did. Agents built from your customer list are still simulated, and an agent that gets through your sign-up shows that the path can be followed, not that your visitors will follow it. They do not predict how many real people will convert. If you have analytics, support tickets, or interview notes, those are stronger evidence for what already happened.
The honest workflow is to use synthetic users to find what is broken and sharpen your questions, then take the rest to real customers or a live experiment.
What you get with Mimiq
- 01
Run it by hand, or on every pull request
Paste a link and a task in the app and watch the agents work. Or run them on every preview deploy: the GitHub Action comments on the pull request, any other CI can call the MCP endpoint, and your coding agent can run it too.
- 02
Bugs and usability problems in one report
The browser records errors and failed or slow requests on the step they happened. The agents say where the wording or the steps confused them, and where they gave up.
- 03
Agents that behave like your users
Each agent is a different user profile with a reason to be there, matched to who the app is for, or built from your own customers.
- 04
A prompt to fix each problem
Every problem comes with a prompt to paste into Cursor, Claude Code, Lovable or any coding agent, and one prompt hands over the top problems at once.
- 05
Follow every agent
See each agent's whole path, step by step with screenshots, and what it was thinking at every step.
The browser records relevant JavaScript errors and failed or slow app requests encountered during the run, attached to the step where they occurred. The agents are simulated users: they find problems, they do not predict how many people will convert.
Questions
Are synthetic users accurate?
They are useful for finding problems, not for numbers. An error, a dead end or a confusing step is on screen whoever hits it, and each finding comes with the step and a screenshot. They do not tell you how many real people will finish.
Are synthetic users the same as real users?
No. They are simulations and should be treated as a fast first pass. Our guide on synthetic users vs real users covers where the two agree and where they diverge.
Can I ask follow-up questions to a synthetic user?
Yes. After a run, ask a follow-up, or interview any one agent about why it stopped or what it expected to see.
How many synthetic users should I run?
A run of 5 agents is the usual start, and you can pick 1, 3, 5, 10 or 20. Each agent uses a real browser, so a few agents stuck at the same step is already a finding.
Can I turn my own customers into synthetic users?
Yes. Import your customers from a CSV and Mimiq builds one agent per row, at one credit per customer. Names, emails and phone numbers are stripped before upload. The agents are still simulated, so treat what they say as leads to check.
Can I use synthetic users for B2B products?
Yes, with care. Agents handle common B2B paths such as sign-up, setup and pricing reasonably well, and with paid credits they can sign in with a test account. They are weaker for narrow expert roles, so treat those results as hypotheses to check with real buyers.
What do synthetic users cost in Mimiq?
Your first test is free: sign in with Google and it starts right away. A free account includes 50 credits, no card. After that, it is a $9 Starter (120 credits that never expire) or monthly plans from $19. An agent uses 5 credits an agent, so 5 agents use 25.
Keep reading
Let the agents find what breaks first.
Your first test is free: sign in with Google and it starts right away. Last checked 2026-10-22.