Describe a business workflow in plain language — create a contract, send it for approval, approve it as another role — and computer-use agents execute it through your real interface, screenshot by screenshot. No Playwright scripts to write, and none to rewrite when the UI changes.
A UI change breaks selectors even when the business process still works — you pay once for the automation and again for its upkeep.
Creator → approver → supplier journeys span screens, roles, approvals and state transitions that are slow by hand and hard to script.
Under sprint pressure the regression suite is shortened or skipped, and defects reach production because nobody had the hours.
Cloud testing tools send screenshots of internal business data to a third party — impossible in a regulated industry.
A scenario is versioned YAML — an objective, a role, assertions and budgets. No selectors, no coordinates, nothing to rewrite after a redesign.
Every step is a screenshot plus the agent’s stated reasoning, persisted whether the step passed or failed.
Creator, approver and supplier each get a fresh, independently authenticated session; run-scoped test data keeps parallel agents from colliding.
A passing run distils its path into step intents — never coordinates — and offers them to later runs as advice, so the suite speeds up with repetition and still trusts the live screen.
Failures are classified as product defect, agent shortcoming, environment problem or assertion mismatch. An agent getting lost is never filed as a bug.
Every model call is metered and aggregated from call to step to scenario to suite, so each run answers what it cost and what it found.
Import your Qase test cases word for word, turn a Qase test run or test plan into a suite, and every result is posted back to the Qase run as its case finishes — with a link to the evidence.
One deployment tests several environments, each with its own address, login and accounts per role. Your team signs in with personal accounts and sees only the environments it has been granted.
Put a suite on a nightly or any cron schedule. A run that finds its environment busy waits its turn instead of being lost, and every run can be compared with the last.
Describe scenarios instead of coding them, and spend the day on triage rather than selector maintenance.
Keep planning in Qase: Argus runs the plan and writes the results back, so coverage, release readiness and what a regression run costs are visible where the team already works.
Debug a failure from screenshots, the agent’s diagnosis and an action-by-action repro trail.
Gate releases on evidence and compare autonomous QA against the manual baseline.
Argus does not replace Playwright. Deterministic scripts keep the stable flows; Argus takes the long, multi-role, frequently-changing ones that are painful to script.
Most AI testing tools use AI to author scripts that are still scripts when they run. Argus reasons about the live screen at every step, and the model behind it is configuration, not code.
Ten containers on your own infrastructure — the whole platform, all three engines — with no external SaaS dependency, so screenshots of internal business data never leave the building.
Engineered by Allmaz, an AI product studio based in Azerbaijan.
Argus QA is measured, not assumed. Run against a live contract-management platform, its best run completed roughly 70% of the scenarios that were not blocked by a real application defect, found three real defects on its own, and cost $0.4–2.3 in model spend for the full suite. That figure is where the engine stands today, not a service level we sell. And it never files a defect on its own authority — a suspected bug becomes a ready-to-file report that a human reviews and submits.
The QA, AI and pentest engines run in one deployment behind one console and one sign-in. They share a browser layer, an encrypted credential store and a model router whose model each engine picks in Settings — so accounts, environments, models and deployment are set up once for all three.
Drives your business workflows through the real interface and reports what broke, with a screenshot and a stated reason behind every step.
Runs thousands of synthetic users against your assistant and scores every conversation with an Azerbaijani-native judge.
Scans your approved targets as a logged-in user, so the check reaches past the login wall, and reports findings by severity.
Allmaz is the AI product studio of Smart Solutions, which built and operates Azerbaijan's unified public procurement portal — established by presidential decree and delivered as one of the country's first public-private partnerships in digital government.
Request a demo to see agents run a multi-role regression suite through your own application.