Stress-test your AI assistant with synthetic users
Stress-test your AI assistant with synthetic users with Argus AI: a practical, on-prem approach built for Azerbaijani teams.
Stress-Test Your AI Assistant with Synthetic Users
Ensure your AI assistant is production-ready by simulating thousands of realistic Azerbaijani users. As one of the three core engines of Argus—a self-hosted AI testing platform—this module operates alongside QA and pentest engines under a single console and sign-in. By utilizing a judge model selected within the system settings, the platform subjects your assistant to diverse personas and adversarial behaviors, identifying critical vulnerabilities and edge cases before they ever reach your customers. The engine treats the assistant under test as a black box, accessing it via connectors such as REST, Dify, Kommunicate, or browser automation to mirror the exact experience of an end-user. By deriving expected behaviors from your uploaded knowledge and policy documents, the system generates a comprehensive evaluation of performance. This process produces a readiness score, detailed findings, and write-once assurance records, providing a transparent and reproducible audit trail of your assistant's stability and compliance.
The Advantages of Synthetic User Testing
Identify critical edge cases using adversarial personas that simulate frustration, contradiction, and manipulation.
Validate linguistic robustness across Azerbaijani-native interactions and complex AZ↔RU code-switching.
Ensure complete data privacy and security through a self-hosted, on-premise deployment model.
Prevent recurring bugs with dedicated regression suites that confirm previously identified issues stay fixed.
Generate immutable, write-once assurance records and readiness scores for every testing cycle.
Protect system stability with bounded per-assistant concurrency to prevent testing from becoming a service attack.
Core Testing Capabilities
Diverse Persona Generation
Generates thousands of synthetic users, each defined by a specific role, goal, language, style, and behavior.
Adversarial Testing
Prioritizes high-risk scenarios such as prompt-injection, contradiction, and manipulation to find breaking points.
Native LLM Judging
An Azerbaijani-native judge model scores responses based on accuracy, tone, formality, compliance, and safety.
Black-Box Connectivity
Tests the assistant via REST, Dify, Kommunicate, or browser automation to simulate actual customer access.
Policy-Driven Expectations
Derives expected behaviors from your uploaded knowledge and policy documents, remaining human-overridable.
The Testing Process
Frequently Asked Questions
How is the platform deployed and managed?
Argus is a self-hosted platform where the testing engine, QA engine, and pentest engine are all managed behind a single console and sign-in for streamlined administration.
Will the testing process crash my assistant?
No. Per-assistant concurrency is strictly bounded to ensure that the stress-testing process does not inadvertently become a denial-of-service attack on your infrastructure.
Is the readiness score a definitive release gate?
The readiness score serves as a signal rather than a strict release gate, as the judge model does not yet have a published agreement measurement against human reviewers.
How does the system handle scoring consistency over time?
Each test run snapshots its evaluator configuration at launch. This ensures that a finished run is never re-scored against a model chosen after the run was completed.
What makes the synthetic users 'adversarial'?
Unlike polite users, adversarial personas are designed to test limits through prompt-injection, manipulation, and AZ↔RU code-switching, as these are the scenarios most likely to break an assistant.
Ready to harden your AI assistant?
Deploy Argus to stress-test your assistant with realistic Azerbaijani synthetic users today.
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