Autonomous regression testing

Regression testing that runs itself.

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.

The problem

Why Argus QA exists

Scripts break
Brittle automation

A UI change breaks selectors even when the business process still works — you pay once for the automation and again for its upkeep.

Multi-role
Long workflows

Creator → approver → supplier journeys span screens, roles, approvals and state transitions that are slow by hand and hard to script.

Every release
Regression gets cut

Under sprint pressure the regression suite is shortened or skipped, and defects reach production because nobody had the hours.

Your screenshots
SaaS is a non-starter

Cloud testing tools send screenshots of internal business data to a third party — impossible in a regulated industry.

What it does

Built to do the job, end to end

🎯

Objectives, not scripts

A scenario is versioned YAML — an objective, a role, assertions and budgets. No selectors, no coordinates, nothing to rewrite after a redesign.

👁️

Observe, reason, act

Every step is a screenshot plus the agent’s stated reasoning, persisted whether the step passed or failed.

👥

Multi-role workflows

Creator, approver and supplier each get a fresh, independently authenticated session; run-scoped test data keeps parallel agents from colliding.

🧠

Route memory

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.

🏷️

Honest failures

Failures are classified as product defect, agent shortcoming, environment problem or assertion mismatch. An agent getting lost is never filed as a bug.

💵

Cost per test

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.

Explore in depth

Argus QA capabilities

All Argus QA capabilities →

How it works

From input to outcome

1Describe the workflow as a scenario objective
2Argus reads the build stamp and runs an environment preflight
3Computer-use agents execute the workflow through the real UI
4Every step is captured as a screenshot with the agent’s reasoning
5You get a report with evidence, classified failures and cost
Who it's for

Made for the people who use it

QA engineers

Describe scenarios instead of coding them, and spend the day on triage rather than selector maintenance.

QA leads

See coverage, release readiness and what a regression run actually costs.

Developers

Debug a failure from screenshots, the agent’s diagnosis and an action-by-action repro trail.

Engineering managers

Gate releases on evidence and compare autonomous QA against the manual baseline.

Why Argus QA

What sets it apart

A third QA layer

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.

Agentic at run time

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.

Self-hosted

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.

An Allmaz product

Engineered by Allmaz, an AI product studio based in Azerbaijan.

Deployment & trust

Yours to control

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.

Learn more

Argus QA — topics, use cases & comparisons

One platform, three engines

Argus is one deployment, not three tools.

The QA, AI and pentest engines run on the same runtime and share a browser layer, a model router, an encrypted credential store and a single cost ledger — so models, credentials and deployment are configured once for all three, and whatever any of them finds lands in the same evidence trail.

Backed by Smart Solutions

Built by the company that runs national infrastructure.

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.

9,000+companies use Smart Solutions products
15+years of national-scale delivery, since 2006
150+people across the group
See the track record →
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See Argus QA on your own data

Request a demo to see agents run a multi-role regression suite through your own application.