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. Allmaz provides an autonomous regression testing solution designed to handle high contact-center volumes and churn pressure by ensuring enterprise web applications remain stable through AI-driven computer-use agents. By shifting from fragile coordinate-based scripts to reasoning-based agents, operators can maintain high availability and performance across complex subscriber portals. This solution integrates seamlessly into the enterprise ecosystem as one of the three engines of Argus, a self-hosted AI testing platform. It allows QA teams to configure models, credentials, and cost accounting once across QA, AI, and pentest engines. By utilizing a model-agnostic architecture and a two-tier execution strategy, the platform ensures that regression testing is both scalable and cost-effective, providing a robust safety net for frequently changing web interfaces without the overhead of constant manual script maintenance.
Solving Telecom Quality Challenges
Maintain strict service-quality SLAs across high-volume subscriber portals through autonomous regression.
Validate complex, multi-role workflows involving creators, approvers, and suppliers in independent browser sessions.
Reduce manual testing overhead for mixed AZ/RU subscriber conversation flows using AI reasoning.
Prevent subscriber churn by identifying product defects independently before they reach production.
Scale parallel testing across multiple environments without data collisions using run-scoped naming.
Optimize operational spend with a two-tier model execution strategy and detailed cost aggregation.
Enterprise-Grade Testing Capabilities
AI Computer-Use Agents
Agents observe screenshots and reason through actions based on YAML objectives rather than fragile selectors or coordinates, allowing for flexible testing of frequently changing interfaces.
Multi-Role Workflow Validation
Simulate complex telecom operations where different roles run in fresh, independently authenticated browser sessions to ensure end-to-end process integrity.
Deterministic Security
Logins are performed by scripts using an encrypted secret store, ensuring the AI agent never handles raw credentials.
Independent Verification
To ensure honesty and accuracy, assertions are checked by an independent verifier rather than the agent that performed the action.
Model-Agnostic Architecture
Switch between different models via database configuration without redeploying code, utilizing a two-tier execution strategy to optimize costs.
The Autonomous Testing Workflow
Frequently Asked Questions
Does this replace our existing Playwright scripts?
No, it complements them. Use deterministic scripts for stable flows and AI agents for long, multi-role, or frequently changing scenarios.
How is the data hosted and secured?
The platform is self-hosted using ten containers and a shared artifact volume, removing external SaaS dependencies.
How are costs managed for AI model usage?
Every model call is metered, and costs are aggregated from the call level up to the scenario, suite, and release.
Can it handle API testing?
Cases tagged as API tests are skipped during import, as the browser agent is specifically designed for UI interactions.
How does the AI handle failures and bug reporting?
Failures are classified (e.g., product defect, timeout, or agent failure). The AI flags suspected bugs with a report, but a human must review and file it to Jira or Qase.
Ensure Your Subscriber Experience is Flawless
Deploy autonomous regression testing to protect your SLAs and reduce churn. Contact Allmaz for a technical deep dive.
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