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 Agentic systems are stochastic, context-dependent, and policy-bounded. Conventional QA—unit tests, static prompts, or scalar “LLM-as-a-judge” scores—fails to expose multi-turn vulnerabilities and provides weak audit trails. Developer teams need protocol-accurate conversations, explicit policy checks, and machine-readable evidence that can gate releases with confidence. Qualifire AI has open-sourced Rogue, a Python framework that evaluates AI agents over
The post Qualifire AI Releases Rogue: An End-to-End Agentic AI Testing Framework, Evaluating the Performance of AI Agents appeared first on MarkTechPost. Read More  

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