Does AI insurance cover losses when your agent deals with another company's agent?

Q: Does AI insurance cover losses when your agent deals with another company's AI agent?

Not as a defined, named product as of mid-2026. AIUC covers faulty tool actions and hallucination-driven loss from your own deployed agent. Armilla covers performance and liability failures identified through its own governance assessment of the agent it is underwriting. Neither publishes a specific coverage grant for a loss that arose because two independently governed AI agents, deployed by two different businesses, jointly produced a bad transaction. Where cover responds at all, it is because the loss falls within your own agent's ordinary failure modes, not because a multi-agent coverage clause exists.

Q: What is a multi-party AI liability chain and why does it matter for coverage?

A multi-party AI liability chain describes a loss where more than one AI system, and more than one legal entity, contributed to the outcome: a foundation model provider, a fine-tuning layer, and one or more deployers whose agents interacted. It matters for coverage because underwriters price risk they can measure, and a chain with two independently built, independently governed agents is materially harder to price than a single agent's failure rate. This category is explicitly recognised in current market analysis as a gap that is not yet widely underwritten by any named carrier.

Q: What should I ask an insurer about agent-to-agent risk before buying a policy?

Ask directly whether the policy responds to a loss where your AI agent transacted with a counterparty's AI agent and the outcome was adverse, and ask for the specific clause reference, not a verbal assurance. Ask whether the policy requires the counterparty's system to also meet a defined governance standard for cover to apply. Ask whether any sublimit or exclusion applies specifically to autonomous negotiation or agent-initiated contract formation. As of 2026 the honest expectation is that most insurers will not have a ready answer, which is itself useful information about how early this risk category is.

Q: How does the EU AI Act affect coverage for agent-to-agent transactions?

The EU AI Act (Regulation (EU) 2024/1689) does not create a specific rule for agent-to-agent commerce, but Article 25 on value chain responsibilities and Article 26 on deployer obligations both remain relevant to who is accountable in a multi-party AI failure, and Article 14 on human oversight is directly relevant to whether a deployer can show it supervised an agent's negotiating authority. The revised Product Liability Directive (Directive (EU) 2024/2853), applying from 9 December 2026, brings defective AI software inside strict liability, which becomes relevant where a genuine system defect, not an ordinary bad negotiation, caused the loss. Underwriters are watching this regulatory layer closely because it will shape who they expect to hold accountable before they price a multi-agent product.

By the Agent Insured Editorial Desk 27 July 2026 Reading time: 12 minutes

Short answer

Not as a named, defined coverage grant. As of mid-2026, none of the active AI liability underwriters, including AIUC, Armilla, Counterpart, or Munich Re through aiSure, publish a product built specifically for the case where two independently deployed AI agents, belonging to two different businesses, jointly produce a bad transaction. Where a policy responds at all, it is because the loss falls within your own agent's ordinary failure modes, such as a faulty autonomous tool action or a hallucination-driven error, not because a multi-agent clause exists.

This gap is explicitly recognised in current market analysis of AI insurance risk categories: multi-party liability chains, where a model provider, a fine-tuner, and more than one deployer contributed to a failure, are flagged as not yet widely covered. Future Proof Intelligence publishes this as an independent reading of the market and is not an insurer or a broker.

Key takeaways

No named AI insurer currently sells a defined coverage grant for losses arising specifically from two independently deployed AI agents transacting with each other. Cover, where it exists, comes from your own agent's ordinary failure modes.
Multi-party liability chains, where more than one AI system and more than one legal entity contributed to a loss, are explicitly identified in current market analysis as a category not yet widely underwritten.
The underwriting reason is structural: an insurer can price a single agent's failure rate with enough data, but pricing the joint behaviour of two independently governed systems requires visibility into both, which no single insurer currently has.
The EU AI Act's value chain provisions (Article 25) and deployer obligations (Article 26), alongside the human oversight requirement in Article 14, remain the relevant accountability framework even though no AI-specific agent-to-agent rule exists.
The practical step for an operator is to ask an insurer directly, in writing, whether agent-to-agent transaction risk is covered, rather than assume an existing AI liability or E&O policy already extends to it.

A question the market has not been asked enough yet

Coverage analysis in this category has, so far, mostly addressed a single-agent question: does a policy respond when your AI agent makes a mistake, a question covered in depth in who insures AI agents in Europe on this site. That is the shape of the market's current products. AIUC underwrites hallucination-driven loss, data leakage, IP issues, and faulty tool actions arising from a single insured deployment. Armilla evaluates and covers the AI model it is underwriting against defined performance and governance criteria. Munich Re's aiSure pays on a measurable performance trigger for the system it has agreed terms on. Each of these is built around one agent, one deployer, one policy.

What none of them currently answer cleanly is the increasingly common scenario where your AI agent is not making an isolated mistake, but transacting directly with another company's AI agent, and the loss emerges from the interaction between the two rather than from either system failing in isolation. A procurement agent that negotiates a bad price with a supplier's pricing agent has not necessarily malfunctioned; it may have executed a technically valid negotiation that produced a commercially bad outcome because of how the two systems interacted. That is a different underwriting problem from a single hallucinating chatbot, and the market has not yet built a named product for it.

Why this gap exists, structurally

Underwriters price what they can measure, and the entire current generation of AI liability products is built around measuring one deployer's system. AIUC's audit process runs thousands of adversarial simulations against the specific agent it is certifying. Armilla's governance evaluation assesses the specific model and its controls before quoting. Both approaches assume the insurer has, or can obtain, visibility into the system whose risk it is pricing.

A multi-party liability chain breaks that assumption immediately. If your agent and a counterparty's agent jointly produce a bad transaction, pricing that risk properly would require visibility into both systems, both governance regimes, and both companies' documentation, which no single insurer currently has access to and no current product is structured to obtain. This is precisely the category current market analysis of AI insurable risk identifies as an open gap: multi-party liability chains, where a model provider, a fine-tuning layer, and one or more deployers all contributed to a failure, sit outside what AIUC, Armilla, Counterpart, and Munich Re currently underwrite as a defined product.

The insurers who have built products for a single agent's mistakes have not yet built one for what happens when two agents, governed by two different companies, jointly produce a bad outcome. That gap is not a secret. It is simply early.

What actually might respond, and what will not

The honest answer is not that an operator has no protection at all, only that the protection is incidental rather than purpose-built. The table below sets out where existing product categories are likely to help and where they are likely to stop short.

Product	Likely to respond	Likely gap for agent-to-agent loss
AIUC-1-backed policy	A faulty tool action or hallucination by your own agent that happened to occur during a negotiation	The joint outcome of two independently governed agents interacting is outside the single-agent audit scope
Armilla / Lloyd's programme	A performance failure or governance gap in the specific agent Armilla assessed and priced	No assessment or visibility into the counterparty's agent, so joint causation is not underwritten
Munich Re aiSure	Your own system underperforming against the agreed specification	Not designed to allocate fault between two separate companies' systems
Errors and omissions / professional indemnity	Possibly, if a human retained oversight and the loss resembles ordinary professional negligence	Many wordings now carry explicit AI exclusions added at recent renewals

The pattern echoes a related gap covered in does AI insurance cover third-party tool or plugin failure: the further a loss sits from a single, cleanly attributable system, the less current products are built to reach it. The practical reading of this table is not that agent-to-agent risk is uninsurable forever. It is that the risk currently falls into the space between products, where each insurer's scope stops just short of the joint interaction that actually caused the loss. That space closes as the market matures, in the same sequence every new risk category has closed historically: first the single-party product, then the data to price the multi-party version, then the multi-party product itself.

The regulatory layer that will shape whatever gets built

Even without an AI-specific agent-to-agent rule, the existing EU framework already shapes who insurers will expect to hold accountable when a multi-party product eventually appears. Article 25 of the EU AI Act (Regulation (EU) 2024/1689) addresses value chain responsibilities, relevant wherever more than one party contributed to an AI system's behaviour. Article 26 places operating duties on the deployer of an AI system, and Article 14 requires human oversight proportionate to risk, which is directly relevant to whether a deployer can demonstrate it supervised an agent's negotiating authority rather than leaving it fully autonomous. Separately, the revised Product Liability Directive (Directive (EU) 2024/2853), applying to products placed on the market after 9 December 2026, brings defective AI software inside strict liability, which becomes the relevant framework where a genuine system defect, rather than an ordinary bad-but-valid negotiation, caused the loss.

Underwriters building toward a multi-party product will need this accountability structure settled before they can price it, because pricing requires knowing who answers first when a joint failure occurs. The clearer the deployer obligations and value chain responsibilities become through EU AI Act enforcement from August 2026 onward, the more tractable the underwriting problem becomes. This is one reason the insurance market and the regulatory timeline are moving in step rather than independently.

What an operator should actually do about it now

Given that no purpose-built product exists yet, the practical response is documentation and direct questioning rather than waiting for a product to appear. Ask any AI liability insurer you are already talking to, in writing, whether the policy responds to a loss arising from your agent's transaction with a counterparty's AI system, and request the specific clause reference rather than a verbal assurance. Ask whether any sublimit or exclusion applies specifically to autonomous negotiation or agent-initiated contract formation. If the honest answer from the insurer is that this has not been considered, that is itself useful information about how early the category is, and a reason to build strong internal documentation of your agent's authority limits in the meantime, since that documentation is exactly the evidence an insurer will eventually want once a multi-party product is built. For the practical version of that documentation, aimed at SME operators specifically, see the companion analysis on insureyouragent.com, and for how a structured readiness assessment produces the evidence file insurers and regulators both want, see the Agent Certified readiness assessment.

Frequently asked questions

Does AI insurance cover losses when your agent deals with another company's AI agent?

Not as a defined, named product as of mid-2026. AIUC, Armilla, Counterpart, and Munich Re's aiSure each cover a defined set of failure modes for a single deployed agent. Where a policy responds to an agent-to-agent transaction loss, it is because the loss falls within your own agent's ordinary failure modes, not because a multi-agent coverage clause exists.

What is a multi-party AI liability chain and why does it matter for coverage?

It describes a loss where more than one AI system and more than one legal entity contributed to the outcome, such as a foundation model provider, a fine-tuning layer, and two separate deployers whose agents interacted. It matters because underwriters price risk they can measure, and pricing a chain involving two independently governed systems requires visibility neither insurer currently has into the other side.

Will my AI liability policy pay if the other side's AI system was at fault?

Most current policies are written around your own deployed system's behaviour, not around apportioning fault between two companies' AI systems. The counterparty's fault is typically a separate legal question for that counterparty and its own insurer, rather than something your policy adjudicates or reduces your own liability for automatically.

What should I ask an insurer about agent-to-agent risk before buying a policy?

Ask directly, in writing, whether the policy responds to a loss where your AI agent transacted with a counterparty's AI agent, and request the specific clause reference. Ask whether the policy requires the counterparty's system to meet a defined governance standard for cover to apply, and whether any sublimit or exclusion applies specifically to autonomous negotiation.

How does the EU AI Act affect coverage for agent-to-agent transactions?

There is no AI Act rule written specifically for agent-to-agent commerce, but Article 25 on value chain responsibilities, Article 26 on deployer obligations, and Article 14 on human oversight remain the relevant accountability framework. The revised Product Liability Directive (Directive (EU) 2024/2853), from 9 December 2026, adds strict liability exposure where a genuine AI system defect, rather than an ordinary bad negotiation, caused the loss.

References

Regulation (EU) 2024/1689 of the European Parliament and of the Council (the Artificial Intelligence Act). Article 14 (human oversight), Article 25 (value chain responsibilities), Article 26 (deployer obligations).
Directive (EU) 2024/2853 on liability for defective products (revised Product Liability Directive). Applies to products placed on the market after 9 December 2026. Brings software and AI systems inside strict product liability with rebuttable presumptions of defectiveness.
AIUC (Artificial Intelligence Underwriting Company). AIUC-1 standard and bundled audit-and-coverage model. Coverage scope: hallucination-driven loss, data leakage, IP infringement, harmful outputs, and faulty autonomous tool actions arising from a single audited deployment. aiuc.com.
Armilla, managing general agent and Lloyd's coverholder. AI liability and performance cover paired with governance evaluation of the specific model being underwritten. armilla.ai.
Munich Re aiSure, AI performance insurance settling on a measurable, agreed performance trigger for the insured system.
Counterpart, Affirmative AI Coverage, launched November 2025, scoped to Miscellaneous Professional Liability, Allied Health, and Tech Errors and Omissions for a single insured entity's AI use.
Market analysis of AI insurable risk categories identifying multi-party liability chains, where a model provider, a fine-tuning layer, and a deployer all contributed to a failure, as a risk type not yet widely covered by any named carrier. See Future Proof Intelligence, AI Agent Insurance Market Landscape.
For the operator-facing companion analysis of this same gap, see insureyouragent.com. Future Proof Intelligence publishes this analysis as an independent reading of the market and is not an insurer or a broker.