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

arXiv: https://arxiv.org/abs/2607.13230 · HTML: https://mlsanity.com/paper/2607.13230/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 38 references that are in the map.
- First posted on arXiv 14 Jul 2026.

## Builds on (most cited first, 9 of 9)

- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Ignore Previous Prompt: Attack Techniques For Language Models](https://mlsanity.com/paper/2211.09527/) (2022)
- [Insurance of Agentic AI](https://mlsanity.com/paper/2606.05449/) (2026)
- [Mitigating Moral Hazard in Cyber Insurance Using Risk Preference Design](https://mlsanity.com/paper/2203.12001/) (2022)
- [The Internet of Agentic AI: Communication, Coordination, and Collective Intelligence at Scale](https://mlsanity.com/paper/2606.12835/) (2026)
- [A Tale of Two Mechanisms: Incentivizing Investments in Security Games](https://mlsanity.com/paper/1503.07377/) (2015)
- [Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes](https://mlsanity.com/paper/2105.01792/) (2021)
- [Cyber Insurance for Cyber Resilience](https://mlsanity.com/paper/2312.02921/) (2023)

## Abstract (arXiv)

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.