From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
A new framework, CPSAINT, is introduced for agentic AI, offering a seven-layer integrity decomposition that spans physical state, sensors, data, compute, actuators, environment, and time. Paired with the FRIESA-K residual-risk functional, the approach maps specific failure paths to quantified risk instances using a controlled absorbing Markov model, enabling formal, composable risk assessment. The framework is demonstrated on both a warehouse robot and a financial-services agent, maintaining consistent semantics and structure across domains.
Why it matters: This work provides a formal and composable method to quantify residual risk in agentic AI systems, addressing a key challenge in AI safety and governance.
Full story at: arXiv AI/ML ↗