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This paper proposes CPSAINT, a seven-layer integrity decomposition for agentic AI systems, paired with FRIESA-K, a residual-risk functional based on absorbing Markov models. It bridges structural failure path analysis with quantified risk estimates, demonstrating the framework on warehouse robotics and financial-services agent scenarios.
Introduces AgentRevive, a Markov state-aware framework for resilient multi-agent collaboration that uses soft state transitions (Active, Standby, Terminated) to prevent premature pruning of agents that may recover, reducing token consumption while improving performance on reasoning and domain tasks.