🚀 Why Traditional AI Testing Isn’t Enough
Agentic AI systems, those that act and make decisions, are complex beasts. They’re unpredictable, context-dependent, and bound by policies. Current quality assurance methods—like unit tests or simple prompts—just don’t cut it. They can’t expose multi-turn vulnerabilities or provide solid audit trails. Developer teams need more: realistic conversations, explicit policy checks, and machine-readable evidence to confidently release their AI agents.
🌟 Introducing Rogue – The AI Testing Revolution
Qualifire AI has just open-sourced Rogue, a Python framework that evaluates AI agents over the Agent-to-Agent (A2A) protocol. Rogue transforms business policies into executable scenarios, engages in multi-turn interactions with target agents, and generates deterministic reports perfect for CI/CD and compliance reviews.
💻 Getting Started with Rogue
Prerequisites:
– `uvx` (install using `uv` installation guide if not already installed)
– Python 3.10+
– An API key for an LLM provider (e.g., OpenAI, Google, Anthropic)
Installation:
– Quick Install (Recommended):
“`
# TUI
uvx rogue-ai
# Web UI
uvx rogue-ai ui
# CLI / CI/CD
uvx rogue-ai cli
“`
– Manual Installation:
1. Clone the repository:
“`
git clone https://github.com/qualifire-dev/rogue.git
cd rogue
“`
2. Install dependencies using `uv sync` or `pip install -e .`
3. Optionally: Set up your environment variables in a `.env` file.
Running Rogue
Rogue works on a client-server architecture. The default behavior starts the Rogue server in the background and launches the Terminal User Interface (TUI) client. You can also run specific modes like Server, TUI, Web UI, or CLI.
Example: Testing the T-Shirt Store Agent
Rogue comes with a simple example agent that sells T-shirts. You can use it to see Rogue in action. Here’s how:
1. Install example dependencies using `uv sync –group examples` or `pip install -e .[examples]`.
2. Start the example agent server in a separate terminal.
3. Configure Rogue in the UI to point to the example agent.
4. Run the evaluation using either the TUI or Web UI mode.
Where Rogue Fits: Practical Use Cases
– Safety & Compliance: Validate data handling, refusal behavior, and secret-leak prevention.
– E-Commerce & Support: Enforce discounts, refund rules, and tool-use correctness.
– Developer/DevOps: Assess code-mod and CLI copilots for workspace confinement and unsafe command prevention.
– Multi-Agent Systems: Verify planner-executor contracts and interoperability.
– Regression & Drift Monitoring: Detect behavioral drift and enforce policy-critical pass criteria.
What is Rogue, and Why Should Agent Dev Teams Care?
Rogue is an end-to-end testing framework that evaluates AI agents’ performance, compliance, and reliability. It turns business context and risk into structured tests with clear objectives and success criteria. Rogue provides streaming observability and deterministic artifacts, making it an invaluable tool for agent development teams.
Under the Hood: How Rogue Is Built
Rogue operates on a client-server architecture, with multiple interfaces connecting to the core evaluation logic running in the backend server.
Summary
Rogue helps developer teams test agent behavior as it runs in production. It turns written policies into concrete scenarios, exercises those scenarios over A2A, and records what happened with transcripts you can audit. The result is a clear, repeatable signal you can use in CI/CD to catch policy breaks and regressions before they ship.
Find Rogue on GitHub
Thanks to the Qualifire team for their thought leadership and support in creating this article.
