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 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.