From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
Existing approaches either describe failure mechanisms without transferable risk estimates or produce risk estimates treating failure paths as black boxes. This paper proposes CPSAINT, a seven-layer integrity decomposition, paired with FRIESA-K, a residual-risk functional that maps each failure path to a quantified risk instance, providing a mechanism-to-magnitude pipeline for resilient agentic AI.
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[Submitted on 28 Apr 2026]
Title:From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI
View a PDF of the paper titled From Agent Failure Paths to Quantified Residual Risk: A Compositional Framework for Resilient Agentic AI, by Hassan Karim and 3 other authors
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Abstract: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 treating the internal failure path as a black box. We couple those two views by proposing CPSAINT, a seven-layer integrity decomposition over Physical state, Sensors, Data, Compute, Actuators, Environment, and Time, paired with FRIESA-K, a residual-risk functional that maps each failure path to a quantified risk instance. FRIESA-K grounds the resistance term K in a controlled absorbing Markov model so that control effectiveness is derived from state dynamics rather than assigned as an informal score. The result is a concise mechanism-to magnitude pipeline for resilient agentic and embodied AI. We report governance observability through a separate additive penalty instead of inserting governance as a new variable in the resistance functional. We formalize structural composability linking valid failure paths to well-defined risk instances and show the framework on two contrasting scenarios a hard real-time warehouse robot and a governance-instrumented financial-services agent. Across both cases, the same layer grammar, variable semantics, and dynamic-resistance construction remain intact. Thus, we obtain a compact kernel that supports cross-domain reasoning, explicit assumptions, and quantitatively grounded formalism of composable trust.
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.18243 [cs.AI]
(or arXiv:2607.18243v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.18243
arXiv-issued DOI via DataCite
Submission history
From: Deepti Gupta [view email] [v1] Tue, 28 Apr 2026 23:40:24 UTC (294 KB)
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