# Which AI-Agent Coverage Exclusions Could Leave Businesses Uncovered in 2026?

Amelia Palmer · September 28, 2026

> What AI-Agent Exclusions Mean for Businesses Businesses buying insurance for autonomous or semi-autonomous AI systems should assume that “AI...

## What AI-Agent Exclusions Mean for Businesses

Businesses buying insurance for autonomous or semi-autonomous AI systems should assume that “AI coverage” does not automatically mean protection against every error, abuse, or loss caused by an agent. Insurers are increasingly separating losses caused by conventional software from losses arising from agentic behavior, including unauthorized decisions, manipulation of users, regulatory violations, and interactions with third-party tools. As of 28 September 2026, this distinction matters because exclusions proposed or applied during 2026 could leave a company responsible for costs that a general cyber policy may not have been intended to insure. Coverage may also depend on whether the AI tool is a product, a service, or an employee-like operational delegate.

**Also worth reading:** [Are AI Insurance Exclusions Driving More Litigation in 2026, and What Should Technology Businesses Do?](https://in-surely.com/knowledge/are_ai_insurance_exclusions_driving_more_litigation_in_2026_and_what_should_technology_businesses_do.php) · [How Do Businesses Get Insurance Coverage for AI Agents in 2026?](https://in-surely.com/knowledge/how_do_businesses_get_insurance_coverage_for_ai_agents_in_2026.php) · [Are AI Policy Exclusions Limiting Coverage for AI Agents, Robotics, and Generative AI?](https://in-surely.com/knowledge/are_ai_policy_exclusions_limiting_coverage_for_ai_agents_robotics_and_generative_ai.php)

A standard cyber policy often responds to familiar events such as unauthorized access, theft of records, ransomware, or a business interruption following a covered intrusion. An AI agent can create a different loss pattern: it may make a plausible but erroneous financial transaction, send confidential information to the wrong recipient, bypass an approval process, or continuously alter customer-facing decisions. Those outcomes can involve errors and omissions liability, privacy claims, unfair-discrimination claims, contractual penalties, or purely financial losses rather than a traditional breach. The central question is therefore not whether AI is mentioned in the policy, but whether the insured event falls within an identified coverage grant and survives all applicable exclusions.

There is no universal AI-agent exclusion and no reliable percentage of policies containing one. Terms vary by insurer, jurisdiction, industry, and the degree of autonomy granted to the system. A low-risk internal assistant with human approval may be treated differently from an agent that can independently initiate payments, negotiate contracts, access production systems, or communicate with customers. Businesses should treat policy language, application answers, technical controls, and the actual permissions of the agent as parts of one risk-control system rather than reviewing the insurance contract in isolation.

## Why Agentic AI Creates Different Insurance Exposure

The first reason is the range of decisions an agent can make. Conventional software generally performs functions that developers define, while an agent can interpret instructions, select tools, plan multi-step actions, and respond to changing context. That flexibility can improve productivity, but it also makes the chain of responsibility harder to predict. Insurers may therefore ask what model was used, whether retrieval data was approved, which tools the agent could call, what transaction limits applied, and whether a human could stop the action before loss occurred.

The second issue is scale. A human employee who processes 20 transactions incorrectly may cause limited damage, whereas an agent connected to customer-service systems can attempt 20,000 incorrect actions within an hour. Insurers and regulators are paying closer attention to this operational concentration, particularly where an organization gives one model broad access to email, cloud infrastructure, payment platforms, or customer records. Boston Consulting Group’s analysis of agentic AI in commercial property and casualty insurance emphasizes always-on portfolio management, but the same always-on character creates continuous exposure outside insurance. A continuously operating system does not automatically imply continuous coverage.

The third issue is third-party dependence. Many agents combine a foundation model, external data, software plug-ins, payment providers, and human contractors. A loss can be triggered by a poisoned document, a compromised plug-in, an incorrect API response, or a supplier’s security failure. The policy may allocate those losses differently depending on whether the event is characterized as cyberattack, accidental error, product defect, or contractual failure. Businesses should not assume responsibility transfers to an AI vendor merely because the vendor supplied the model or software; contractual indemnities can be limited by caps, exclusions, insolvency risk, and the wording of the service agreement.

Finally, legal uncertainty is increasing. The Insurance Business America report on rogue AI agents references concern that developers may be liable when agents cause harm, while Bloomberg Law News has reported alarm among policyholders about gaps created by insurer AI exclusions. Those reports do not establish one legal rule for every jurisdiction, but they show why businesses should document governance. Liability may arise from negligence, breach of duty, discrimination, misuse of personal information, or an agent’s authority exceeding the organization’s instructions.

## Common Exclusions and Coverage Gaps

Cyber-related exclusions often receive the most attention, but they are not the only concern. A policy may exclude losses arising from failure to maintain security, inadequate testing, failure to update software, or acts that are deemed intentional or reckless. Those clauses can matter when an agent acts outside its intended purpose. An insurer may also exclude “emerging technology” losses, contractual liability, fines and penalties, regulatory investigation costs, data ownership disputes, intellectual-property infringement, and loss of data or profits. The exact effect depends heavily on the definitions and exceptions surrounding each clause.

A particularly important gap is the distinction between direct financial loss and consequential loss. Suppose an agent incorrectly transfers funds, overpays a supplier, or fails to file a tax return. Some policies cover only restoration of systems or data following an insured event, not every business expense caused by the event. Other policies may cover business interruption but require the interruption to result from a covered peril. If a claim is based on incorrect output rather than an interruption, the insurer might argue that the traditional trigger was never met.

A second gap concerns fines, penalties, and regulatory costs. These amounts may be uninsurable in some jurisdictions, and even where they are insurable, policies can exclude them or provide only limited defense coverage. A company may also incur costs to notify affected people, investigate a regulator, restore its reputation, or offer customer remediation. Those expenses should be mapped separately rather than assumed to fall within a cyber sublimit.

A third gap is unauthorized or adversarial use. If employees give an agent access credentials, override its controls, use it for prohibited purposes, or conceal known problems, the insurer may rely on an unauthorized-use or misrepresentation defense. This does not mean ordinary employee mistakes defeat every claim, but the business should be able to show that permissions were restricted, logs were retained, and incidents were escalated. A security control that exists only in a slide deck is weaker evidence than access logs, approval records, and test results.

| Coverage issue | Typical policy position | Business risk | Evidence to request |
| --- | --- | --- | --- |
| Unauthorized agent action | May be excluded as misuse, error, or an unauthorized method | Fraud, bad transactions, operational loss | Tool permissions, transaction limits, approval logs |
| Model or data failure | Often treated as software error rather than a covered cyber event | Incorrect output or restart costs | Model inventory, testing records, data provenance |
| Third-party tools | Coverage may depend on the triggering component | Supplier-caused loss or shared responsibility | Vendor contracts, indemnities, incident records |
| Fines and penalties | Frequently limited or excluded | Regulatory orders and legal costs | Compliance controls, jurisdiction analysis |
| Business interruption | Usually requires a covered underlying event | Lost revenue without a covered cyber event | Architecture diagram and recovery test |
| IP or discrimination claims | May require separate E&O or specialist protection | Claims, injunctions, remediation | Governance, review procedures, model documentation |

## Comparing the Main Protection Options
There is usually no single policy that covers every agentic AI loss. Businesses commonly compare standalone cyber insurance, technology errors and omissions coverage, cyber coverage with an AI endorsement, and specialist agentic-AI or robotics insurance. The right choice depends on whether the principal concern is malicious intrusion, erroneous professional output, physical damage from a robot, or financial loss caused by an autonomous transaction. Buying several policies can create overlap, but overlapping policies may also produce competing investigations, different definitions, and coordination problems.

Cyber insurance is often the first layer because it can address privacy incidents, ransomware, network compromise, and related business interruption. Its weakness is that an incorrect decision by a properly functioning model may not be a cyber event. Technology E&O insurance may respond when a system produces defective advice, code, or output and that output causes a client claim, but it can exclude underlying cyber events or require the insured to prove a specific standard of care. A specialist policy may provide more explicit treatment of AI agents, yet it may be narrower, newly priced, or offered only for certain sectors.

| Feature | Cyber policy | Technology E&O policy | Specialist AI or robotics cover |
| --- | --- | --- | --- |
| Main trigger | Security incident or covered intrusion | Defective technology output or service | Agent or robotic-system event, depending on wording |
| Common strength | Breach, ransomware, notification and interruption | Client claims caused by technology error | AI-specific wording and risk controls |
| Common weakness | Pure model error may not qualify | Limits and exclusions may vary by output | New products, narrower underwriting and possible exclusions |
| Evidence needed | Security controls, logs and recovery process | Testing, specifications and client communications | Agent permissions, autonomy level, monitoring and human oversight |
| Potential use | Baseline digital-risk protection | Professional and product liability | High-risk or high-autonomy deployments |

The comparison should be built around an actual loss scenario. If a customer loses money because an agent executes an unauthorized purchase, the organization should ask which policy responds to the transaction, whether prior authorization matters, and whether financial loss is sublimited. If a customer claims that automated underwriting was biased, the relevant questions may concern E&O, discrimination, privacy, and regulatory defense rather than cyber response. If a warehouse robot injures a worker, workers’ compensation, product liability, robotics cover, and property insurance may all need review.
Brokers can be valuable in identifying these differences, but the market should not be treated as uniform. The Show HN description of Goodfault as insurance for AI agents and robots and reports about new products covering AI damage show that specialist capacity is developing. They do not prove that every AI risk is insurable or that a specialist policy is cheaper. The broker should explain the carrier’s definitions, exclusions, limits, deductibles, claims process, and underwriting information in writing before the client relies on a product description.

## Practical Steps Before Buying or Renewing Coverage

Start by creating an inventory of every AI system in use, including shadow tools and vendor-operated agents. For each system, record the model provider, business purpose, data accessed, connected applications, users, countries served, and the maximum value or authority the agent can exercise. A useful threshold is the point at which a mistaken action could cause more than the organization’s ordinary tolerance for error; for financial transactions, that might be a $10,000 per-action limit, while a customer-service agent may need a lower ceiling. These numbers should reflect the business’s own risk appetite rather than an industry rule.

Next, test the policy against several concrete scenarios. Ask the insurer, in writing, how it treats unauthorized payments, incorrect decisions, manipulated prompts, poisoned data, model-provider failures, compromised plug-ins, human overrides, and regulatory penalties. Request definitions for “agent,” “autonomous,” “software error,” “security incident,” and “emerging technology.” A policy that covers the system only when it is used exactly as described in the application can be materially narrower than a policy that covers later ordinary updates or approved changes.

Then align technical and insurance controls. Agents should have least-privilege access, allowlisted tools, spending limits, approval gates, logging, monitoring, and a tested kill switch. High-impact actions should require human confirmation until performance and governance are proven. The organization should also maintain incident-response procedures specifically for AI, including how to pause the agent, preserve prompts and outputs, notify affected parties, and contact the insurer. These measures do not eliminate coverage disputes, but they can improve underwriting terms and demonstrate that the insured recognized the exposure.

Finally, review contracts with model providers, cloud platforms, data suppliers, integrators, and customers. Look for indemnities, defense obligations, exclusions, limitation-of-liability caps, audit rights, notification duties, and provisions governing training data. A vendor promise to “accept responsibility for the AI” may mean only responsibility for its own service, subject to technical exclusions and a cap. Insurers often want to see these contracts because they reveal which party can prevent, detect, and correct a failure.

## Pricing, Limits, and When to Act

Pricing is not standardized enough to quote responsibly without knowing the deployment. Insurers may price from revenue, transaction volume, data sensitivity, industry, geography, control maturity, and the agent’s autonomy. A customer-service copilot with read-only access and a $100 daily spending cap may receive a different treatment from an agent authorized to move $1 million, alter production infrastructure, or make binding purchasing decisions. Asking for a generic price can produce misleading comparisons, so the quote should identify the limit, deductible, coverage form, endorsement, security controls, and any sublimit that applies.

Businesses should also distinguish annual premium from maximum loss exposure. A policy with a $1 million limit and a $250,000 deductible is materially different from a $5 million program with a $50,000 deductible, even if both are described as AI insurance. Aggregate limits may apply to privacy, regulatory, notification, or financial-loss claims, and a single incident may exhaust part of the available limit. The broker should explain whether defense costs erode limits, whether the insurer has a consent-to-settle requirement, and whether the policy is claims-made or occurrence-based.

The timing issue is more important than waiting for a headline. An agent should be reviewed before it is connected to payment, customer, employment, healthcare, credit, or production systems. This matters especially when the organization can make high-impact decisions with limited human review. Insurers and regulators may ask whether the business tested the tool, documented its intended use, and established a process for reports of harm. A review after an incident can be seen as reactive and may reduce the information available for underwriting.

Businesses with no material AI deployment should still record whether employees use unapproved tools, because that activity may be difficult to discover through conventional procurement records. Companies already using agents should obtain a coverage gap review before renewing cyber, E&O, professional-liability, property, or product policies. The review should be repeated after a material model change, new tool connection, acquisition, expansion into another country, or increase in transaction authority. A policy that was adequate for a read-only assistant should not automatically be assumed adequate after that assistant becomes operational infrastructure.

## Common Mistakes and the Best Time to Seek Advice

One common mistake is treating a coverage grant as proof that every resulting loss is covered. The grant describes the event or liability for which the insurer may respond, while exclusions, definitions, conditions, and sublimits narrow or remove that response. Another mistake is assuming that a cyber breach caused by an agent is automatically an agentic-AI loss. The carrier may characterize the event as ordinary cyber negligence, or it may apply an AI-specific exclusion, depending on the wording. The same facts can produce different outcomes under policies written for different risks.

A second mistake is asking only whether the policy “covers AI.” The more useful question is whether it covers the particular loss: incorrect output, unauthorized transaction, third-party claim, physical injury, stolen data, lost revenue, regulatory investigation, or contractual penalty. Businesses should test at least five scenarios against the full policy, including one in which a human approves the final action and one in which the agent acts without human approval. They should also test a compromised data source and a model-provider error, because those facts may be treated differently.

A third mistake is buying a specialist product without comparing it with existing coverage. An AI or robotics policy can fill a real gap, but it may duplicate cyber or E&O cover, contain an exclusion for the most important loss, or apply only to products sold to third parties. The best time to seek advice is before deployment or renewal, when the broker can still influence risk controls, wording, limits, and the accuracy of the application. After a claim, advice is still useful, but immediate notice to every potentially relevant insurer is essential and policy language may impose strict deadlines.

Ultimately, AI-agent insurance is a contract and control decision rather than a label. Businesses should identify what autonomy means in their own environment, calculate the plausible loss from each permission, and obtain written confirmation of how the relevant policies treat that scenario. No insurer can remove every technical, legal, or commercial risk, and some jurisdictions may restrict cover for fines or penalties. The strongest position is a documented program in which technical safeguards, vendor agreements, insurance, and claims planning describe the same system and its real authority.

## Quick answers

### Does cyber insurance usually cover a rogue AI agent?

Not necessarily. Cyber policies commonly cover specified security incidents, privacy events, and resulting interruption, but an incorrect decision or unauthorized action by an agent may be treated as an exclusion, contractual loss, or non-cyber error. The result depends on the policy trigger, the agent’s permissions, and whether a security failure contributed to the event.

### Are AI-agent exclusions common in 2026?

Insurers and brokers are increasingly discussing exclusions for agentic behavior, emerging technology, unauthorized use, and losses caused by model or data failures, but there is no reliable market-wide percentage. Some policies address AI expressly, while others respond through general cyber, E&O, product, or liability wording. Businesses should request the exact definitions rather than rely on market descriptions.

### What is the most important control for reducing agentic AI insurance risk?

The most useful control is to limit the agent’s authority through least-privilege access, allowlisted tools, transaction thresholds, human approval gates, logging, and a tested shutdown process. These controls reduce both the chance and the potential size of a loss, while giving insurers evidence that the business supervised the deployment.

### How much does insurance for AI agents cost?

There is no single public price because premiums depend on revenue, transaction volume, data sensitivity, autonomy, geography, industry, controls, limits, and deductibles. A read-only customer-service assistant and an agent authorized to make high-value purchases cannot reasonably be priced alike. Obtain a written quote that states the coverage form, limit, deductible, sublimits, exclusions, and security requirements.

### Should a company buy both AI insurance and cyber insurance?

It may need both, depending on the risk. Cyber cover can address intrusion, privacy, ransomware, and related interruption, while E&O or specialist AI cover may address defective output or unauthorized decisions. The policies should be compared for triggers, exclusions, limits, and coordination, because a specialist policy can still leave cyber or regulatory losses uncovered.

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