Solutions · Argus QA

Autonomous regression testing for Banking

Autonomous regression testing for banking. Banks operate under Central Bank of Azerbaijan supervision and banking-secrecy rules, so customer data cannot go to foreign clouds.

Autonomous Regression Testing for Banking

Ensure the stability of your banking applications while adhering to Central Bank of Azerbaijan supervision and strict banking-secrecy rules. Our self-hosted AI testing platform enables financial institutions to automate complex regression testing without sending sensitive customer data to foreign clouds. By utilizing AI computer-use agents, the system handles autonomous testing for enterprise web applications, ensuring that critical banking workflows remain functional across updates. Unlike traditional automation, this platform focuses on objective-based testing using versioned YAML scenarios rather than fragile selectors or coordinates. It is designed to complement existing tools like Playwright, utilizing deterministic scripts for stable flows while deploying AI agents for long, multi-role, and frequently changing scenarios. This hybrid approach allows banks to maintain high velocity in their release cycles without compromising the rigorous security standards required for financial infrastructure.

Capabilities

Solving Banking Compliance and QA Challenges

Maintain strict data residency and banking-secrecy obligations via a fully self-hosted deployment of ten containers with no external SaaS dependency.

Reduce manual effort in detecting defects through autonomous scenario execution that observes screenshots and reasons through state changes.

Generate detailed evidence for audit and regulatory compliance with persisted step logs and build stamps for every environment under test.

Manage complex banking processes with multi-role workflows, allowing creators, approvers, and suppliers to run in independent, authenticated sessions.

Optimize operational expenditure using a two-tier model execution strategy that only escalates to stronger models upon agent-side failure.

Prevent environment collisions during parallel testing through run-scoped test data naming, ensuring stability in shared banking environments.

Enterprise-Grade Testing Capabilities

Self-Hosted Infrastructure

Deployed as ten containers with a shared artifact volume, eliminating external SaaS dependencies to keep data within your controlled environment.

Multi-Role Workflows

Simulate complex banking processes where creators, approvers, and suppliers each operate in fresh, independently authenticated browser sessions.

Secure Credential Management

Logins are performed deterministically by script, never by the AI agent, with credentials resolved through an encrypted secret store.

Independent Verification

To ensure objectivity, assertions are checked by an independent verifier rather than the agent that performed the action.

Model-Agnostic Routing

Switch models via database configuration without redeploying code, utilizing a router that probes provider endpoints for vision and grounding support.

The Autonomous Testing Process

1Define scenarios as versioned YAML objectives specifying roles, assertions, and budgets instead of fragile selectors.
2The AI agent observes screenshots, reasons through the state, and takes one action per turn to reach the objective.
3Parallel agents utilize run-scoped test data naming to prevent collisions in shared banking environments.
4The system employs a two-tier model approach, escalating to a stronger model only if the initial cheap model fails.
5Failures are classified (e.g., product defect, environment failure, or timeout) and suspected bugs are flagged for human review.

Frequently Asked Questions

How does the system handle banking secrecy and data privacy?

The platform is entirely self-hosted, meaning no customer data is sent to foreign clouds, ensuring compliance with local regulatory requirements and banking-secrecy rules.

Does the AI automatically report bugs to production?

No. The AI flags suspected product bugs with a ready-to-file report, but a human must review and file the defect; the AI never asserts a defect on its own authority.

Does this replace existing tools like Playwright?

It complements them. Use deterministic scripts for stable flows and AI agents for long, multi-role, or frequently changing scenarios.

How is the cost of AI model usage managed?

Every model call is metered, with costs aggregated from the individual call up to the scenario, suite, and release level for full transparency.

How does the system handle model updates or changes?

Model selection lives in the database and is read per run. Changing a model takes effect on the next unit of work without requiring any code redeployment.

Secure Your Banking Infrastructure

Implement autonomous regression testing that respects your regulatory obligations. Contact Allmaz to learn more about our self-hosted AI testing engines.

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