Solutions · Argus QA

Autonomous regression testing for Telecom

Autonomous regression testing for telecom. Operators handle millions of subscriber interactions across Azerbaijani and Russian, under service-quality SLAs.

Autonomous Regression Testing for Telecom Operators

Telecom operators manage millions of subscriber interactions across Azerbaijani and Russian languages while adhering to strict service-quality SLAs. To maintain these standards, Allmaz provides an autonomous regression testing solution designed to handle the complexity of high-volume contact centers and the pressure of subscriber retention. By leveraging AI computer-use agents, the platform ensures that enterprise web applications remain stable and performant, preventing critical regressions from reaching the end subscriber. As one of the three core engines of the Argus self-hosted platform, this solution integrates seamlessly with QA, AI, and pentest engines through a unified configuration for models, credentials, and cost accounting. It moves beyond traditional testing by utilizing versioned YAML objectives rather than fragile selectors, allowing the system to reason through complex workflows and identify product defects independently. This approach enables operators to scale their quality assurance without the overhead of constant script maintenance.

Capabilities

Solving Telecom Quality Assurance Challenges

Maintain service-quality SLAs through autonomous regression testing of complex subscriber portals and internal tools.

Reduce churn and retention pressure by identifying product defects before they impact the subscriber experience.

Execute complex multi-role workflows where creator, approver, and supplier roles operate in fresh, independently authenticated sessions.

Eliminate maintenance overhead by using reasoning-based agents instead of fragile coordinates or CSS selectors.

Control operational expenditure via a two-tier model execution strategy and granular, aggregated cost accounting.

Ensure total data sovereignty and security through a self-hosted deployment with no external SaaS dependencies.

Enterprise-Grade Testing Capabilities

AI Computer-Use Agents

Agents observe screenshots and state their reasoning to take actions based on YAML objectives, moving away from coordinate-based testing.

Multi-Role Workflow Support

Simulate complex telecom operations where different roles run in fresh, independently authenticated browser sessions.

Deterministic Security

Logins are performed by scripts using an encrypted secret store, ensuring agents never handle raw credentials.

Independent Verification

Assertions are checked by an independent verifier rather than the agent performing the work to ensure objective results.

Model-Agnostic Architecture

Switch models via database configuration without redeploying code, with live probing of provider capabilities.

Collision-Free Parallelism

Run-scoped test data naming allows multiple agents to operate in a shared environment without colliding.

The Autonomous Testing Process

1Define scenarios as versioned YAML objectives including roles, assertions, and time budgets.
2The agent observes the application screenshot, reasons through the next step, and executes one action per turn.
3The system utilizes a two-tier model approach, escalating to a stronger model only if the initial cheap model fails.
4Failures are classified into categories such as product defect, agent failure, or environment timeout.
5If a product bug is suspected, the system generates a ready-to-file report for human review and filing.
6Successful runs distill their path into step intents, providing advisory guidance for future test executions.

Frequently Asked Questions

Does this replace existing tools like Playwright?

No, it complements them. Use deterministic scripts for stable flows and AI agents for long, multi-role, or frequently changing scenarios.

How is the AI's authority handled regarding bug reporting?

The AI never asserts a defect on its own authority; it flags suspected bugs with a report that a human must review and file.

How is the cost of AI model usage managed?

Every model call is metered, and costs are aggregated from the call level up to the scenario, suite, and release.

Is the solution cloud-based?

The solution is self-hosted using ten containers and a shared artifact volume, removing external SaaS dependencies.

How does the system handle model updates and compatibility?

Model selection is managed in the database and read per run, requiring no redeployment. The system probes provider endpoints live to verify vision support and coordinate grounding.

Stabilize Your Subscriber Experience

Deploy autonomous regression testing to protect your SLAs and reduce churn. Contact Allmaz to learn more about our self-hosted AI testing platform.

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