Agentic end-to-end testing: an AI agent tests your site
End-to-end testing with an AI agent means the agent does the exploring, planning, and running — no test scripts to write, no selectors to maintain. It walks real user journeys, from discovery to checkout, and returns verified bug reports with evidence instead of a red or green status.
What agentic end-to-end testing is
Agentic QA turns the test suite upside down. Instead of a human deciding in advance which journeys matter and writing a script for each one, an AI testing agent decides while it works. It treats your live site like a real user would: it finds the entry points, figures out what a visitor could do, and actually does it.
Automated website testing this way covers the journeys you already know about — and can surface issues in journeys nobody remembered to script.
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1
Discover
The agent crawls representative pages and finds the key journeys — navigation paths, forms, search, checkout — on desktop and mobile.
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2
Plan
It decides what to verify for each journey: what a real user would do, what could go wrong, and which steps matter most.
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3
Act
It performs the steps in a real browser — clicking, typing, submitting, scrolling — just like a visitor would.
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4
Observe
It watches what actually happened: page state, console errors, network responses, missing elements, layout breaks.
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5
Adapt
When something unexpected happens, it adjusts its plan instead of failing scripted steps — the way a human tester would react.
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6
Report
It returns verified bug reports: what broke, where, and with evidence you can look at — screenshots, console logs, and traces.
Two ways to run it
Mira Checks offers two modes of agentic end-to-end testing, and they complement each other:
General auto-discovery
Give the agent the sitemap and let it discover the site on its own. It samples representative pages, finds the key journeys, and tests what a visitor would do — without you defining a single test.
This is the best starting point: zero setup, and it catches issues in journeys you didn't think to write down.
Targeted no-code workflow tests
Describe a critical workflow in plain language — a goal plus the expected result — and the agent walks it end to end. Think of it like a Playwright test without writing code: the agent figures out the steps instead of a script.
Use this when a specific journey matters more than the rest — checkout, onboarding, booking — and you want the agent to focus exactly there.
Both modes sample your site rather than exhaustively covering it, and targeted workflow tests use natural-language goals with expected results rather than hard-coded deterministic assertions. No recorded flows, no exported test suites to maintain.
Reports you can verify, not just trust
A scripted suite ends with "pass" or "fail" — a single bit of information. An agent ends with findings, and each finding carries the evidence that produced it:
See the broken state the agent saw, at the moment it saw it.
The JavaScript errors and warnings that accompanied the failure.
The exact steps and interactions that led to the finding, so you can reproduce it.
One honest caveat that applies to every AI testing agent: model-assisted findings can be wrong, and a clean run does not mean a website is bug-free, secure, accessible, or release-safe. Treat the report as strong evidence to act on — not a certification.
An automated accessibility audit as part of the run
As the agent visits pages, it also runs an automated accessibility audit against the WCAG 2.1 A/AA success criteria that tools like Axe can check automatically — catching missing labels, contrast issues, and keyboard-trapping elements in an automated audit, not a full compliance review.
The audit covers the pages the agent visits, which means it doesn't crawl your whole application and doesn't reach pages behind authentication. And because automation can't judge everything a human auditor can, a clean accessibility run is a useful signal — not a WCAG certification.
Why teams switch to agentic end-to-end testing
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No suite to write or maintain
No selectors that break when the markup changes, no flaky tests to debug. The agent adapts to the site as it is today.
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Finds what nobody scripted
Discovered journeys surface issues in flows your team stopped thinking about — the quiet, high-risk paths.
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Starts in minutes, not sprints
Auto-discovery needs no setup. Targeted workflow tests need a sentence, not a spec.
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Evidence-backed findings
Findings ship with screenshots, console logs, and traces — so you fix fast instead of reproducing first.
See it in action: an AI agent walking a checkout journey · how Mira Checks works · Mira Checks vs. scripted monitoring tools
Frequently asked questions
- What is agentic end-to-end testing?
- Agentic end-to-end testing is automated website testing driven by an AI testing agent. Instead of a scripted suite, an agent explores the real site, plans what to verify, performs actions, observes the results, adapts as it goes, and returns verified bug reports with evidence. There are no test scripts to write or maintain.
- How is agentic end-to-end testing different from scripted test automation?
- Scripted test automation needs a human to write and maintain selectors and assertions, one test per journey. An agentic testing agent figures out what to test by exploring the site, then runs journeys on its own. Think of it like a Playwright test without writing code — that is an analogy, not an exact equivalence: an agent plans dynamically, while a script always runs the same steps.
- If the agent finds no issues, is my website bug-free?
- No. Agentic end-to-end testing samples representative pages and key journeys, so a clean run does not guarantee a website is bug-free, secure, accessible, or release-safe. Findings are also model-assisted and, like any AI output, can be wrong. Treat a clean report as a strong signal, not a guarantee.
Let an AI agent test your site end to end
Mira is live. Point it at your site and get verified bug reports in minutes — no test scripts to write.
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