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 resilient and reliable before deployment. As one of the three core engines of Argus—a self-hosted AI testing platform—this engine shares its runtime, model layer, credential store, and cost ledger with the QA and pentest engines to provide a unified testing ecosystem. By generating thousands of realistic Azerbaijani synthetic users, the platform simulates complex real-world interactions to identify vulnerabilities and behavioral gaps within a secure, on-prem environment. Unlike standard testing, this engine treats adversarial personas as first-class citizens. It simulates high-risk behaviors including frustration, contradiction, prompt-injection, manipulation, and AZ–RU code-switching, recognizing that polite users are the least likely to expose system flaws. By testing the assistant as a black box through actual customer-facing connectors, Argus provides a rigorous validation process that ensures your AI can handle the unpredictability of human interaction while remaining compliant with your internal policies.
Advantages of Synthetic User Testing
Identify critical failures using adversarial personas before they impact real customers
Maintain total data sovereignty through a fully self-hosted AI testing architecture
Validate assistant behavior against uploaded knowledge and policy documents
Prevent regressions using automated suites that confirm past issues remain fixed
Test the actual customer-facing interface via black-box connectors like REST or browser automation
Scale testing to thousands of diverse users without the overhead of manual QA
Core Capabilities
Diverse Synthetic Personas
Generates users with specific roles, goals, styles, and knowledge levels, including adversarial personas that utilize frustration, contradiction, and AZ–RU code-switching.
Azerbaijani-Native LLM Judge
An integrated judge scores the assistant on accuracy, tone, formality, compliance, and safety based on native language nuances.
Black-Box Connectivity
Tests the assistant exactly as a customer would via REST, Dify, Kommunicate, or browser automation.
Dynamic Expectation Mapping
Expected behaviors are derived from your uploaded policy and knowledge documents, remaining human-overridable.
Immutable Run Snapshots
Each test run snapshots its evaluator configuration at launch to ensure consistent scoring and historical accuracy.
The Testing Process
Frequently Asked Questions
How does the platform handle adversarial testing?
The engine prioritizes adversarial personas, simulating prompt-injection, manipulation, and contradiction to find the edge cases most likely to break an assistant.
Is the readiness score a definitive release gate?
The readiness score serves as a signal for quality rather than a hard release gate, as there is currently no published agreement measurement against human reviewers.
Can the testing process crash my AI assistant?
No. Per-assistant concurrency is bounded to ensure that stress-testing does not inadvertently become a denial-of-service attack on the system.
How are the expected behaviors determined?
Expectations are proposed based on your uploaded knowledge and policy documents, but they remain human-overridable to ensure accuracy.
How is scoring consistency maintained over time?
Each run snapshots its evaluator configuration at launch, ensuring that a finished run is never re-scored against a model chosen after the test was completed.
Ready to harden your AI assistant?
Deploy Argus to stress-test your AI with synthetic users and secure your customer experience.
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