Use cases · Argus QA

Run regression tests after every sprint

Run regression tests after every sprint with Argus QA: a practical, on-prem approach built for Azerbaijani teams.

Autonomous Regression Testing for Enterprise Web Applications

Argus QA transforms enterprise quality assurance by deploying AI computer-use agents to perform autonomous regression testing. Unlike traditional automation that relies on fragile CSS selectors or fixed coordinates, Argus utilizes versioned YAML objectives. These objectives define roles, assertions, and step/time budgets, allowing the agent to observe screenshots, reason through the current state, and execute actions one turn at a time. This shift from rigid scripting to objective-based navigation ensures that testing remains resilient even as user interfaces evolve across frequent sprint cycles. As a core engine of the broader Argus self-hosted platform, the QA module integrates seamlessly with shared configurations for credentials, cost accounting, and deployment. It is designed to complement existing deterministic tools like Playwright, handling the long, complex, and multi-role workflows that are typically too costly to maintain manually. By persisting every step and recording environment build stamps, Argus provides a transparent, auditable trail of execution that bridges the gap between autonomous exploration and rigorous enterprise validation.

Capabilities

Key Advantages of AI-Driven Regression

Eliminate selector fragility by using YAML-based objectives instead of hard-coded coordinates or DOM selectors

Execute complex multi-role workflows where creators, approvers, and suppliers operate in independent, authenticated browser sessions

Optimize operational spend via a two-tier model execution strategy that only escalates to stronger models upon agent failure

Ensure total data privacy and sovereignty with a self-hosted, containerized deployment that removes external SaaS dependencies

Accelerate bug resolution with ready-to-file reports that provide human reviewers with the necessary context to confirm product defects

Enable high-concurrency testing in shared environments through run-scoped test data naming to prevent agent collisions

Enterprise-Grade Capabilities

Model-Agnostic Architecture

Configure reasoning effort and provider routing via the database. Model selection is read per run, allowing updates to take effect on the next unit of work without redeployment.

Deterministic Security

Authentication is handled by deterministic scripts rather than the AI agent, with all credentials resolved through a secure, encrypted secret store.

Independent Verification

To prevent self-confirmation bias, assertions are validated by an independent verifier rather than the agent that performed the action.

Route Memory

Passing runs distill their successful paths into step intents—not coordinates—which serve as advisory guidance for subsequent executions.

Granular Cost Accounting

Every model call is metered and aggregated from the individual call level up through steps, scenarios, suites, and full releases.

The Autonomous Testing Workflow

1Define scenarios as versioned YAML objectives specifying roles, assertions, and execution budgets.
2The agent observes a screenshot, states its reasoning, and executes one action per turn, persisting every step.
3The system employs a two-tier model approach, utilizing a cost-effective model first and escalating to a stronger model only for agent-side failures.
4An independent verifier checks assertions and classifies outcomes as product defects, agent failures, environment issues, or timeouts.
5Suspected product bugs are flagged with a ready-to-file report for human review and final submission.

Frequently Asked Questions

Does Argus QA replace existing tools like Playwright?

No, it is designed to complement them. Use deterministic scripts for stable, unchanging flows and AI agents for long, multi-role, or frequently changing scenarios.

How is the system deployed and managed?

It is a self-hosted solution consisting of ten containers and a shared artifact volume, ensuring no dependency on external SaaS providers.

How does the AI handle sensitive login credentials?

The AI agent never performs the login. Logins are executed by deterministic scripts using credentials retrieved from an encrypted secret store.

Can the AI autonomously file bugs in our tracking system?

No. To maintain human oversight, the AI flags suspected bugs with a ready-to-file report, but a human must review and officially file the defect.

How does the system handle different AI models?

It is model-agnostic. It probes provider endpoints for vision and coordinate support and allows model switching via the database without requiring code redeployment.

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