AI user testing: what it is and when to use it

Most teams need feedback before they have volume, and they ship faster than they can recruit. A new sign-up has no users yet, a preview has never been used by anyone outside the team, and a recruited study takes days to schedule. AI user testing fills that gap with a fast, repeatable first pass: can someone new get through, and what breaks on the way?

• Paste a link and a task in plain words. No test scripts.
• Each agent uses your app in its own real browser: it clicks, scrolls, types and fills in forms.
• See who finished, who got partway and who gave up, and the issues ranked by how many agents hit them.
• Ask a follow-up or interview any one agent to learn why it stopped.
• Run it from the app, from your coding agent, or on every preview deploy.

AI user testing uses AI agents as stand-ins for your users. Each agent has a profile, such as a role, an age and a country, and a task. It looks at its screen, decides what to do next, and acts in a real browser the way a person would: it clicks, scrolls, types and fills in forms, including an email address with a working inbox, so verification codes and magic links reach it.

The useful output is not one verdict. It is the spread. When several agents with different needs try the same sign-up, some finish, some get partway, and some give up. A line like "3 of 5 stopped at the company size field" tells you more than any score, and the reasons agents give for stopping are usually the most actionable part of the report.

This is different from asking a chatbot for feedback. A chatbot tells you what it thinks of a description or a screenshot. An agent actually uses the app, so it finds the error that appears on screen, the button that does nothing and the form that rejects a valid input, with the step and a screenshot to show it.

Errors on screen, buttons and links that do nothing, forms that reject good input, dead ends, steps where agents give up, and wording that sends people the wrong way. Each issue comes with the steps, a screenshot and a suggested fix.

A run takes a link and a task, such as 'Sign up and start using it'. Each agent opens the app in its own real browser and tries it, and stops where a user would. It fits sign-up and onboarding, pricing pages, checkout up to the payment step, booking a demo, and any other path you can put in a sentence.

With paid credits, agents can sign in with a test account you give them and open protected previews (a Vercel bypass secret or a site password). The secrets are filled in only in the browser and deleted when the run ends. Test accounts and protected previews come with paid credits: the $9 Starter or a plan from $19 a month. Agents cannot pay with real cards, and a CAPTCHA or bot protection can stop them. Any agent stopped by one is marked as blocked by the site, and if it stops every agent, the run fails and nothing is charged. A staging or preview address without bot protection works, as long as it is public or you give access.

In the app: paste a link and a task, and watch the agents work. This is the quickest way to try it.

From your coding agent: Mimiq's MCP server at mcp.mimiqai.com lets tools like Claude Code, Cursor and Codex send agents through a preview they just built and read back what the agents hit.

On every preview deploy: a GitHub Action runs the agents on each Vercel, Netlify or Cloudflare preview and keeps one comment on the pull request.

A report is where the questions start. Ask a follow-up, or interview any one agent about what it saw and why it stopped.

Then fix what stopped them and send the same agents through again with the same task, and compare the two runs. Your coding agent can do the same through the MCP server.

Be precise about what a run supports. Every agent in a Mimiq report is simulated. A run shows what is broken or confusing and where agents get stuck. It does not tell you how many real people will convert, and it is not a measurement of your visitors.

A clean run means nothing obvious was broken for those agents. It does not mean the flow works for everyone. For the conversion number, ship and measure; for real people's voices, run research with real people.

Agents are strongest on problems that stop whoever tries: something broken, something that does nothing, a step with no way forward. They are weakest on niche expert audiences, emotional or lived experience, and anything that needs a real card or a body.

Do not use it as the final word on a high-stakes decision. A pricing change for an established product, a regulated flow, or a rebrand deserves interviews with real customers and a live experiment once you have traffic.

Do not use it for questions that depend on a real person's body or history: accessibility with assistive technology, physical product handling, or how a patient feels moving through a diagnosis flow. Those need real participants.

Where it earns its place is earlier: before the first real user, on every preview, and whenever a team is debating a flow with opinions instead of evidence of what happens when someone tries it.

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. No per-seat pricing.

A run costs 5 credits an agent, so 5 agents use 25. Imported customers cost one credit each to turn into agents.