Every new change should
respect everything that already works.
As your team ships faster with AI, checking that existing functionality still works becomes increasingly important. Qarmiq verifies critical product behavior, catches regressions, and helps keep your checks current as the product evolves.
The faster you build, the more you have to protect.
A change to checkout can affect invoicing. A change to authentication can affect account access. The impact isn’t always obvious from the code diff. Without regular verification, problems can survive until a customer encounters them.
A change is bigger than its diff
Coding agents now make changes across files nobody opened, in minutes. A reviewer can read what changed, but not everything it touched. What needs re-checking is no longer something a person can hold in their head.
Tests are pinned to the screen, not the promise
A script that clicks a selector fails when a button moves, whether or not anything is broken. Regression time goes on repairing tests to learn nothing, while the failures that matter wait in the same queue.
Nobody did anything wrong here. It is simply what building faster does, and the cost falls on whoever has to answer for what still works.
Traditional tests can break even when your product doesn’t.
Conventional UI tests often depend on selectors, buttons, and page layouts. When the interface changes, tests can fail even though the underlying functionality still works. Teams then spend time repairing tests instead of investigating actual product problems.
None of this is a case against automation. Scripts are fine; they just need something above them that knows what the product is for.
A failed test should start an investigation, not end one.
When a script fails, Qarmiq can have an agent recheck the behavior. If the product still works, the script can be updated. If the behavior really broke, the failure can be surfaced with supporting evidence.
What people actually click
Testers test in your own app as they normally do and record pass or fail. Witness keeps the evidence while they work: clicks, network calls, console errors and performance. It never records typed values, and it is not video.
The three levels → The agentCoverage no one has time to write
An AI agent tests a check, or a whole run, in a real browser. A separate AI judge decides pass, fail or inconclusive from the evidence, and a pass produces a Playwright script, proven by repeated runs before it counts.
The autonomous level → The scriptsA script that goes first, then explains itself
On the next run the script goes first. If it fails, the agent re-checks. When the product still works, the script repairs itself. When it does not, you have a real finding rather than a flaky test.
The links between them →What any of them learn is offered on the Decision Board. It becomes part of the suite only when a person accepts it.
Protect what matters. Review what’s new.
Repeatedly verified checks cover known behavior. Autonomous agents can investigate changes and propose additional checks. Your team decides which new findings become part of the ongoing verification process.
Proven checks become verified scripts, so each run starts from what is already known and spends the agent’s effort on what is new.
Mark a scenario as non-negotiable and a failing one opens an alert that closes only once it holds again. Everything else is weighed by a rule across five stability signals.
New checks, learnings and merges are proposed, never applied. A person accepts or dismisses each one, and a dismissed finding is not raised a second time.
Know what broke, why it matters, and where to start.
Findings connect the failure to the expected behavior, the run where it occurred, and the evidence needed to reproduce it. Your developers and coding agents can investigate with a clearer starting point.
Every check, per browser
A run tests a fixed set of scenarios on one environment and records pass, fail, blocked or skipped per check. Each result says whether a person, a script, the agent or an external tool produced it.
See how a run works → The defectWhat broke, which rule, how to reproduce it
A finding links to the scenario, check and run it came from, with an annotated screenshot. An AI developer brief turns the captured evidence into steps your coding agent can act on.
How findings reach developers → The readingOne verdict from eight signals
Pass rate, regression-free, consistency and five more feed a single Stable, Watch or Unstable reading, set by a rule over five stability signals, so a failing one cannot be outvoted by healthy ones.
See the reading →Questions you’re probably asking.
- Why do older regression suites miss new breakage?
- A script suite is written against clicks, selectors and screen layout, so it ages with the screen. It fails when a button moves rather than when a promise breaks, and teams spend regression time repairing tests instead of learning whether the product still works.
- What stops a Qarmiq suite going stale?
- Scenarios state what must always be true in plain sentences, with checks that hold no UI detail. Testers, an autonomous agent and verified Playwright scripts all record results against the same scenario. A script goes first on each run, and when it fails but the product still works, it updates itself.
- Who decides whether a check passed?
- A separate AI judge rules pass, fail or inconclusive from the captured evidence. An answer that cites no evidence counts as inconclusive, and a script never decides a failure by itself.
Start with the part of your product you can’t afford to break.
We’ll help define the critical behaviors, run Qarmiq against them, and show you how it identifies regressions.
Qarmiq is not self-serve yet. We set each team up directly. Pricing is one flat price per project.