AI test execution
A scripted run is a playback. The framework sends step one, then step two, and if the button it was told to click isn't there yet, the step fails. That model works, but it puts the whole burden of anticipating reality on whoever wrote the script: every wait, every retry, every conditional branch for the cookie banner that appears sometimes.
AI execution moves some of that judgment into the run itself. An agent takes the intent of a step, looks at the actual state of the application, and works out how to satisfy it. If a page is still loading, it waits because the content isn't there, not because someone hard-coded five seconds. If a modal it has never seen before is covering the form, it can dismiss it and carry on. And when it reaches an assertion, it evaluates whether what's on screen matches what the test meant, which catches things a string comparison would miss and, honestly, sometimes disagrees with a human about what counts as correct.
The other half of execution is what comes out the end. A scripted failure gives you a stack trace and a screenshot. An agent run can say which step broke, what it saw instead, and whether the cause looks like a product defect or the application simply moving underneath the test, which feeds straight into the work of keeping a suite current. That triage is the difference between a red build somebody investigates and a red build everybody scrolls past.
How it works
- Each step is interpreted against the live state of the application rather than replayed as a fixed command.
- Waiting is driven by what's actually on screen, so timing gaps stop being the main source of flaky results.
- Unexpected interruptions, popups, consent banners, session prompts, can be handled in the run instead of failing it.
- Assertions are evaluated on meaning, not just exact text, and the agent records the reasoning behind the verdict.
- Runs go wide in parallel across browsers, devices, and environments, since nothing about the approach is tied to one machine.
- Failures are sorted into likely defects and likely drift before anyone opens the report.
Where you'll see it
- Every-commit regression runs in CI, triggered from GitHub Actions or a similar pipeline and expected to finish before a merge.
- Cross-browser and cross-device suites, where the same flow behaves slightly differently on each target.
- Nightly runs against staging, where the question in the morning is which failures are real, not which ones are noise.
- Release gates, where a run's verdict has to be trustworthy enough to block a deploy.
Benefits and challenges
| Benefit | Challenge |
|---|---|
| Flakiness from timing and unexpected UI drops sharply, because the run adapts instead of failing. | Two runs of the same test can take slightly different paths, which makes a failure harder to reproduce exactly. |
| Failure reports arrive triaged, so engineers start from a likely cause instead of a stack trace. | An agent's judgment on an ambiguous assertion needs checking, especially early on. It can pass something a person would have caught. |
| Parallel runs scale across browsers and devices without per-target scripting. | Model-backed runs cost more per step than raw script playback, so suite size and run frequency become a budgeting question. |
| The same test survives an interface change that would break a replayed script. | Debugging asks for different tooling, since the useful artifact is the agent's reasoning trace rather than a line number. |
Frequently asked questions
How is this different from just adding smart waits to a scripted test?
Smart waits solve one problem, timing. AI execution also decides how to satisfy a step when the page isn't what the test expected, handles interruptions it was never told about, and judges assertions on meaning. A wait strategy can't do any of that.
Is an AI run slower than a scripted one?
Per step, usually yes, since there's reasoning happening between actions. In practice teams often come out ahead because runs aren't being repeated to chase flaky failures and nobody is spending a morning sorting real bugs from noise. Parallel execution covers most of the rest.
Can I trust it to block a release?
Most teams get there, but not on day one. The usual path is running it alongside an existing suite for a few cycles, comparing verdicts, and promoting it to a release gate once the disagreements are rare and explainable.
How Klarent helps
Klarent runs tests through agents that interpret each step against the live application, heal what has drifted, and hand back a report saying which failures look like real defects. The runs documentation covers how to trigger and read one.