AI Research
From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
Agentic AI is crossing trust boundaries faster than current risk models can represent. Existing approaches provide one of two partial views. They either describe failure mechanisms without producing a transferable residual-risk estimate, or they produce a risk estimate while trea...
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ArXiv CS.AI
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ArXiv CS.AI
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This research addresses the critical gap in agentic AI safety by developing a compositional framework that bridges failure mechanisms with quantifiable residual risk. By decomposing AI integrity into seven layers and mapping failure paths to risk instances, it enables more robust trust and governance for increasingly complex AI systems.
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