Solutions · Argus QA

Autonomous regression testing for Oil, Gas & Energy

Autonomous regression testing for oil, gas & energy. Energy operators manage safety-critical procedures and vast equipment and materials catalogues across field sites.

Autonomous Regression Testing for Energy Infrastructure

Energy operators manage safety-critical procedures and vast equipment and materials catalogues across diverse field sites. Allmaz provides an autonomous testing solution designed to validate complex enterprise web applications, ensuring that safety SOPs and massive master data records remain consistent across software updates. By utilizing AI computer-use agents, the platform moves beyond fragile, selector-based scripts to a reasoning-based approach that observes the UI and executes actions based on high-level objectives. As part of the Argus self-hosted platform, this engine integrates seamlessly with QA and pentest workflows, sharing a unified configuration for models, credentials, and cost accounting. It is specifically engineered for the rigors of enterprise environments, supporting multi-role workflows and strict security protocols. This ensures that the software powering critical energy infrastructure is resilient, verified, and capable of handling the complex interactions between creators, approvers, and suppliers without compromising data integrity.

Capabilities

Solving Energy Sector Software Challenges

Automate validation of complex safety SOPs and runbooks without the need for constant manual script updates

Ensure data consistency across massive equipment and materials master data sets through autonomous verification

Verify multi-vendor supplier records using multi-role workflow testing in independent browser sessions

Eliminate fragile selector-based tests that typically break during UI updates by using AI reasoning and screenshots

Maintain strict security and data sovereignty via a self-hosted deployment with encrypted secret stores

Reduce testing overhead for shift-based field operations software by automating long, frequently changing scenarios

Enterprise-Grade Testing Capabilities

AI Computer-Use Agents

Agents observe screenshots and reason through actions to perform regression testing, moving away from rigid coordinates or selectors.

Multi-Role Workflow Validation

Simulate real-world energy operations where creators, approvers, and suppliers each operate in fresh, independently authenticated browser sessions.

Self-Hosted Infrastructure

Deployed as ten containers with a shared artifact volume, removing external SaaS dependencies for sensitive energy infrastructure data.

Deterministic Security

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

Independent Verification

Assertions are checked by a separate verifier rather than the agent performing the work, ensuring objective validation of safety-critical flows.

The Autonomous Testing Process

1Define scenarios as versioned YAML objectives specifying roles, assertions, and time budgets.
2The agent observes the application UI, states its reasoning, and executes one action per turn.
3Parallel agents use run-scoped data naming to test shared environments without colliding.
4The system employs a two-tier model execution, escalating to stronger models only upon agent-side failure to optimize cost.
5Failures are classified (e.g., product defect, environment failure, or timeout) and suspected bugs are flagged for human review.
6Passing runs distill their path into 'route memory' as advisory guidance for future test executions.

Frequently Asked Questions

Does the AI automatically file bug reports in our system?

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

How does this integrate with existing testing tools like Playwright?

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

Is the system locked into a specific AI model provider?

No, the platform is model-agnostic. Model selection is managed in the database and can be changed per run without redeploying code.

How is the cost of AI model usage tracked?

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

How does the system handle authentication for different user roles?

Login is performed deterministically by script—never by the agent—with credentials resolved through an encrypted secret store to ensure security.

Secure Your Critical Energy Workflows

Implement autonomous regression testing to ensure your safety-critical procedures and material catalogues are always production-ready.

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