Qualifire AI Releases Rogue: An End-to-End Agentic AI Testing Framework, Evaluating the Performance of...

Qualifire AI has released Rogue, an open-source AI testing framework that evaluates the performance of AI agents over the Agent-to-Agent (A2A) protocol. Rogue is designed to address the limitations of conventional QA methods, which often fail to expose multi-turn vulnerabilities and provide weak audit trails. The framework converts business policies into executable scenarios, drives multi-turn interactions against a target agent, and outputs deterministic reports suitable for CI/CD and compliance reviews.

Rogue operates on a client-server architecture, where the core evaluation logic runs in a backend server, and various clients connect to it for different interfaces. The framework is available in several modes, including a default mode that starts the server in the background and launches the TUI client, as well as separate modes for the server, TUI, web UI, and CLI.

To get started with Rogue, developers need to install the uvx and Python 3.10+ prerequisites, as well as an API key for an LLM provider. The framework is available for installation via a quick install script or a manual installation process, which involves cloning the repository and installing dependencies.

Rogue has several features that make it an attractive option for developers, including its ability to convert business policies into executable scenarios, drive multi-turn interactions against a target agent, and output deterministic reports. The framework also supports various modes, including a default mode, server mode, TUI mode, web UI mode, and CLI mode.

Overall, Rogue is a powerful AI testing framework that can help developers evaluate the performance of AI agents over the A2A protocol. Its ability to convert business policies into executable scenarios and drive multi-turn interactions against a target agent make it an attractive option for developers looking to improve the reliability and security of their AI systems.