AI-Native Insurance for Agentic AI: Pricing, Underwriting, and End-to-End Automation

2026-07-14

AI Summary

This study addresses emerging insurability challenges posed by agentic AI systems, whose autonomy, tool use, and environmental interaction introduce risks beyond the scope of traditional insurance. The authors propose a native insurance framework tailored to autonomous agents, modeling key risk dimensions—including autonomy level, operational permissions, permission exposure, governance maturity, and dependency concentration—to quantify event likelihood and loss severity. Within constraints of participation, profitability, and incentive compatibility, the framework optimizes insurance contract design. Structural insights reveal the boundaries of insurability, a monotonic decline in feasibility with increasing exposure, and critical thresholds for governance certification. Notably, this work internalizes insurance as an operational cost and regulatory mechanism in AI deployment. Empirical validation in a healthcare setting demonstrates successful contract optimization, parameter sensitivity analysis, and end-to-end automated claims processing, confirming the framework’s efficacy and practicality.

Abstract

Agentic AI introduces new insurance challenges because autonomous AI systems can make decisions, invoke tools, modify external environments, and interact with third-party services. This paper develops an AI-native mathematical framework for underwriting, pricing, and contract design for agentic AI deployments. A deployment is represented by a risk state that captures autonomy level, operational authority, permission exposure, governance maturity, and dependency concentration. The framework maps the risk state to event probabilities, loss severities, governance costs, premiums, deductibles, coverage allocation, and policy covenants, and formulates an optimization problem for insurance contract design under participation, profitability, and incentive compatibility constraints. The paper establishes structural properties of insurability, including characterization of an insurability region, monotone deterioration of feasibility with increasing exposure, and governance certification thresholds. Insurance is further interpreted as both an operational cost and a regulatory mechanism for AI deployment. A healthcare case study illustrates contract optimization, sensitivity analysis, and automated claims processing for agentic AI systems.

Problem

• Agentic AI
• Insurance
• Risk Assessment
• Autonomous Systems
• AI Governance

Innovation

• Agentic AI
• AI-native insurance
• risk state
• insurance contract optimization
• governance certification