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Agentic methods are stochastic, context-dependent, and policy-bounded. Conventional QA—unit exams, static prompts, or scalar “LLM-as-a-judge” scores—fails to show multi-turn vulnerabilities and gives weak audit trails. Developer groups want protocol-accurate conversations, express coverage checks, and machine-readable proof that may gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI brokers over…

In this tutorial, we construct an AI-powered cryptographic agent system that mixes the power of classical encryption with adaptive intelligence. We design brokers able to performing hybrid encryption with RSA and AES, producing digital signatures, detecting anomalies in message patterns, and intelligently recommending key rotations. As we progress, we witness these autonomous brokers securely set…

Agentic methods are stochastic, context-dependent, and policy-bounded. Conventional QA—unit assessments, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and offers weak audit trails. Developer groups want protocol-accurate conversations, express coverage checks, and machine-readable proof that may gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI brokers over…