Measure what your regression suite costs
Measure what your regression suite costs with Argus QA: a practical, on-prem approach built for Azerbaijani teams.
Autonomous Regression Testing and Cost Intelligence
Argus QA is a self-hosted AI testing platform designed for enterprise web applications, utilizing AI computer-use agents to perform autonomous regression testing. Unlike traditional tools, it replaces fragile selectors and coordinates with versioned YAML objectives that define roles, assertions, and step budgets. The system operates as one of three specialized engines within the Argus ecosystem, providing a secure, containerized environment where models, credentials, and deployment configurations are centralized for QA, AI, and pentesting workflows. Beyond autonomy, Argus QA introduces rigorous financial transparency into the AI testing lifecycle. Every model interaction is metered, allowing organizations to aggregate costs from a single call up to the entire release suite. By combining a two-tier model execution strategy with a model-agnostic routing architecture, teams can optimize their spend without sacrificing reliability, ensuring that high-cost models are only utilized when a cheaper model fails to resolve an agent-side issue.
The Advantages of Argus QA
Granular Cost Transparency: Meter every model call with costs aggregated from the individual step level up to the full release suite.
Reduced Maintenance Overhead: Eliminate the need to maintain CSS selectors or coordinates by using YAML-based objectives and visual reasoning.
Optimized Model Spend: Lower operational costs via a two-tier execution strategy that escalates to stronger models only upon agent failure.
Enterprise-Grade Security: Ensure data privacy with a self-hosted deployment of ten containers and encrypted secret stores for credential management.
Complex Workflow Support: Execute multi-role scenarios where creators, approvers, and suppliers operate in independent, authenticated browser sessions.
Seamless Ecosystem Integration: Import test cases verbatim from Qase.io and post results back automatically with full evidence links.
Core Capabilities
Cost Accounting & Metering
Every model call is metered, allowing costs to be aggregated from individual calls up to the full release suite.
Two-Tier Model Execution
A cost-effective model runs first, with escalation to a stronger model only occurring upon agent-side failures.
Model-Agnostic Routing
Model selection is managed in the database and read per run, enabling model changes without redeploying code.
Multi-Role Workflows
Supports complex scenarios where creators, approvers, and suppliers run in independent, authenticated browser sessions.
Autonomous Agent Reasoning
Agents observe screenshots and state reasoning to take actions, with an independent verifier checking assertions.
From Scenario to Cost Analysis
Frequently Asked Questions
Does the AI automatically file bugs in Jira?
No. The AI flags suspected product bugs with a ready-to-file report; a human must review and manually file the defect to Jira or Qase to ensure accuracy.
How does Argus QA handle authentication?
Login is performed deterministically by script, never by the agent. Credentials are securely resolved through an encrypted secret store.
Is this a replacement for deterministic testing tools?
It complements tools like Playwright. Use deterministic scripts for stable flows and AI agents for long, multi-role, or frequently changing scenarios.
Where is the data hosted and how is it deployed?
Argus QA is entirely self-hosted using ten containers and a shared artifact volume, ensuring there is no external SaaS dependency.
How are model updates handled?
Model selection lives in the database and is read per run. Changing a model takes effect on the next unit of work without requiring a redeployment.
Start Measuring Your Testing Spend
Deploy Argus QA to bring transparency and autonomy to your enterprise regression suite.
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