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Abstract

Agentic AI shifts insurance from covering passive digital assets to covering operational actors that reason, invoke tools, alter external state, and depend on shared model and infrastructure providers. These capabilities generate losses from cyber compromise, autonomous decision error, model drift, dependency outage, professional negligence, regulatory violation, and cyber-physical harm. This paper develops an AI-native framework for underwriting, pricing, and contract design for agentic-AI deployments. Each deployment is represented by a risk state _si_ = ( _i, i, i, gi, vi_ ) capturing autonomy category, operational authority, external-state permissions, governance maturity, and dependency concentration. The framework maps this state into event probabilities, severities, governance costs, risk loadings, coverage incidence, indemnity allocation, and policy covenants, and then formulates a contract-design problem over premiums, deductibles, limits, aggregate exposure, allocation rules, and governance obligations subject to participation, insurer-profitability, and incentive-compatibility constraints. We show that insurability forms a region of the risk-state space, that fixed-terms feasibility deteriorates monotonically as exposure grows, and that a governance threshold certifies a deployment as insurable. The paper further reads insurance as an AI operating cost and regulatory-control instrument that internalizes risk, shapes adoption incentives, and supports mandatory financialresponsibility requirements for higher-risk deployments. A healthcare care-coordination case study solves a finite contract menu and shows how delegated authority, permission exposure, dependency concentration, and governance maturity drive feasibility and pricing, and an automated workflow illustrates how monitoring, trigger evaluation, claims validation, and human escalation operationalize the contract architecture.