AI Agent Insurance Products: The New Wave

AI Insurance

AI Agent Insurance Products: The New Wave, and How to Launch One

Insurers spent 2025 and 2026 writing AI out of standard policies and building a new market to replace it. Here is what Armilla, Munich Re, AIUC, Klaimee, HSB and Testudo actually sell, and what it takes to launch a product like theirs.

By Michał Głomba Guide · 12 min read · Updated 2026

In February 2024 a British Columbia tribunal ordered Air Canada to pay a passenger CA$812. The airline’s website chatbot had told Jake Moffatt he could book a flight and claim a bereavement discount retroactively. He couldn’t. Air Canada argued that the chatbot was, in effect, responsible for its own answers. The tribunal did not accept that, and pointed out the obvious: the bot was part of the airline’s website, so the airline owned what it said.

The money was trivial. The principle was not. Moffatt v. Air Canada settled, cheaply and early, the question every business now has to answer at much larger scale: if your AI gets it wrong, you pay.

And that was a chatbot answering a question. The systems being deployed in 2026 do things. They issue refunds, place orders, move money, change records, write and ship code, and make decisions inside workflows where nobody reads every step. The failure modes are bigger, faster and harder to spot. Insurers worked this out well before most of their customers did, and they have responded in two directions at once: writing AI out of the policies businesses already hold, and building a new market to sell back.

Standard policies are being closed to AI on purpose

The clearest signal came from Verisk, whose ISO policy forms sit underneath a very large share of the world’s property and casualty business. Verisk filed a family of generative AI exclusion endorsements that carriers could start attaching to commercial general liability renewals from 1 January 2026:

CG 40 47. Excludes bodily injury, property damage and personal and advertising injury arising out of generative AI, under both Coverage A and Coverage B.

CG 40 48. The narrower version, Coverage B only, aimed at advertising and personal injury claims from AI-generated content.

CG 35 08. Applies the same exclusion to products and completed operations.

They are optional forms. Carriers choose whether to attach them, and plenty have chosen to. Separately, the Financial Times reported that AIG, Great American and WR Berkley were seeking regulatory approval to exclude AI-related liabilities from corporate policies, with units of Berkshire Hathaway, Travelers and Chubb moving in the same direction.

Technology errors and omissions, the line closest to the risk, is not the safety net people assume either. Tech E&O was written for deterministic software and human-delivered services. Where AI is addressed at all, it is often addressed by sublimit. Armilla has pointed to general technology policies that carry a $25,000 sublimit for AI-related liabilities inside cover that otherwise runs to $5m.

Even the model developers feel it. OpenAI has reportedly arranged roughly $300m of cover for emerging AI risks through the broker Aon, against litigation claiming multiples of that figure. When insurers will not comfortably cover the companies building the technology, the message to everyone deploying it is not subtle.

The reasoning is straightforward. Insurers price from loss history, and there isn’t one. The losses that do exist are correlated in an unpleasant way: if a widely used foundation model degrades or gets jailbroken, thousands of policyholders can have a bad day simultaneously. That is a hard risk to model and an easy one to exclude.

Excluding it, though, leaves a gap that businesses very much want filled. Deloitte’s Center for Financial Services expects AI-specific insurance premiums to grow at roughly 80% a year and reach about $4.8bn globally by 2032. Testudo, one of the new specialist underwriters, says generative AI litigation is up 137% year over year. The exclusions and the new products are the same story told from two ends.

Five shapes the new products take

Almost everything on the market today is a variation on one of five designs. They differ in what triggers a payout, how the risk is assessed, and who actually buys the policy.

1. Affirmative AI liability

Armilla is a managing general agent and Lloyd’s coverholder set up specifically for AI risk. Working with Chaucer and other Lloyd’s underwriters, it launched a standalone AI liability policy in spring 2025 that says out loud what standard policies now exclude: hallucinations, model drift, inaccurate outputs, data leakage, and claims tied to defamation, confidentiality breaches and regulatory violations. Limits reach $25m per organisation.

What makes it interesting is the trigger. Rather than waiting purely for a lawsuit, Armilla underwrites the model’s expected performance and responds when it degrades from that baseline. Chief executive Karthik Ramakrishnan has described it plainly: “We assess the AI model, get comfortable with its probability of degradation, and then compensate if the models degrade.” A chatbot that was right 95% of the time at bind and drops to 85% is a covered event, not a support ticket. Armilla has noted that a policy of this shape could have responded to the Air Canada loss.

It is not a blank cheque. Tom Graham of Chaucer put the underwriting stance simply: “We will be selective, like any other insurance company.”

1. A performance guarantee that pays on a measurement

Munich Re’s aiSure takes the idea further and turns the model itself into the rated object. Munich Re runs technical due diligence on the AI, quantifies how likely and how severe underperformance is, and prices the premium off that robustness assessment. The cover indemnifies consequential financial loss: lost revenue, business interruption costs, legal damages.

Claims settle on measurable performance data rather than through conventional loss adjustment, which makes it behave much more like parametric insurance than like a liability policy. It is model-agnostic, so generative systems are in scope alongside classical machine learning. Munich Re has also put the product into other people’s hands, partnering with Mosaic to reach AI vendors.

1. Certify first, insure second

A third group treats the audit as the product and the policy as what you get afterwards.

The Artificial Intelligence Underwriting Company (AIUC) launched in July 2025 with a $15m seed round led by Nat Friedman’s NFDG, alongside Emergence Capital and Terrain. Its AIUC-1 standard is pitched as SOC 2 for AI agents: a security and risk framework covering the technical, legal and operational safeguards enterprise buyers ask about. Vendors certify to get through procurement, then buy insurance that protects their customers if the agent fails. The certificate opens the door; the policy is what makes the promise credible.

Klaimee, out of Y Combinator’s spring 2026 batch, raised $5.5m in July 2026 to do the same thing for autonomous agents specifically. Every agent submitted for cover goes through automated pre-bind testing: adversarial attacks, penetration testing, behavioural analysis, permission validation and operational stress testing. The output is an insurability score and a remediation report, and then an insurance-backed performance warranty. Klaimee’s argument for existing is that tech E&O and cyber were “designed around deterministic software, data breaches and services delivered by humans”, which is not what an autonomous agent is.

1. An add-on that rides an existing small business policy

The other four designs mostly serve AI vendors and large deployers. HSB, the specialty insurer inside Munich Re Group, went after the long tail instead. On 18 March 2026 it launched AI Liability Insurance for small and mid-sized businesses, covering defence, settlement and judgment costs for third-party claims of bodily injury, property damage, or personal and advertising injury arising from the business’s own AI use. The examples HSB gives are deliberately mundane: an AI-controlled HVAC system that creates a slip hazard, a chatbot that generates faulty appliance installation instructions, AI-written marketing copy that draws a copyright or defamation claim.

HSB’s own survey of 1,000 businesses with 1 to 500 employees found 74% already using AI and 91% planning to. “All types of businesses are using AI to do things more quickly and efficiently,” said Timothy Zeilman, the company’s Global Head of Product Ownership. “At the same time, the AI transformation brings new legal and financial exposures.”

The distribution choice matters as much as the wording. HSB does not sell direct. The coverage attaches to the business policies of its carrier partners, which is a much faster route to a million small businesses than building a brand from scratch.

1. A specialist MGA with Lloyd’s capacity

Testudo launched as an MGA in January 2026 focused on generative AI liability for both vendors and deployers, covering third-party claims from AI outputs including hallucinations and model drift, plus legal costs and damages. By March 2026 it had expanded its programme to $9.25m per insured with Apollo, Atrium and QBE behind it. Peta Kilian, senior innovation underwriter at QBE, framed the appeal for capacity providers as the tooling rather than the wording: helping clients with “AI risk scoring and reporting tools”.

What the five have in common

DesignWhat triggers a payoutHow it is underwrittenWho buys it

Affirmative AI liabilityThird-party claim, or measured performance degradationModel assessment plus conventional liability underwritingEnterprises deploying AI, AI vendors

Performance guaranteeA measured drop below the agreed performance levelTechnical due diligence on the model, premium set by robustnessAI vendors and their customers

Certify then insureAgent failure covered by the warrantyPre-bind testing against a published standardAI vendors selling into enterprise procurement

SME add-onThird-party claim arising from the business’s AI useRated with the underlying business policySmall and mid-sized businesses, through their carrier

Specialist MGAThird-party claim from AI outputsDelegated authority, AI risk scoringVendors and deployers wanting standalone limits

Look past the differences and the same five product decisions keep reappearing:

Underwriting is a technical test, not a questionnaire. The rating inputs are eval results, red-team findings, permission scopes and observed accuracy, not headcount and industry code.

The trigger is a number, not just a lawsuit. Several of these products pay when a measured metric crosses a threshold, which is closer to parametric cover than to traditional liability.

Certification is part of the product. The audit is a revenue line and a sales tool, not an internal step. Buyers want the certificate as much as the cover.

Distribution sits next to the thing being insured. HSB rides carrier policies. AIUC and Klaimee attach to a vendor’s enterprise sales cycle. Nobody is waiting for someone to search for AI insurance.

Data keeps flowing after bind. Audit logs, telemetry and model version changes are policy conditions, because a model that was underwritten in March is a different risk in September.

Why this is awkward on a traditional core system

Every one of those five decisions cuts against the grain of a legacy policy administration system.

Rating engines in most cores are built around a stable set of rating factors, keyed to things like industry classification, revenue and headcount. An AI liability product wants to rate on an insurability score, an accuracy baseline, a model identifier and a permission scope, and it wants to add a n

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