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P&C insurers have an opportunity with agentic AI: automate more decisions, more workflows, and more of the work between them.
Regulators, however, expect insurers to address some fundamental questions:
- Where is AI being used?
- What data does it use and is it acting on?
- Does it influence claims, underwriting, pricing, fraud decisions, or other consequential outcomes?
- How are outputs tested?
- When is human-oversight or review required?
- Can a customer seek clarification or challenge an AI-driven decision?
- Can the insurer reconstruct or explain what happened later?
These are no longer theoretical governance questions. Insurance regulators in the U.S. and across the globe are increasingly setting more examination-ready expectations around AI use across market conduct, claims handling, privacy, cybersecurity, unfair discrimination, record retention, and third-party risk.
Meanwhile, insurers desire the efficiency AI can deliver.
They want AI agents that can gather missing claim information, coordinate vendors, identify suspicious activity, prepare customer communications, route exceptions, initiate workflows, and push straightforward work toward resolution without a human shepherding every step.
The problem isn’t a shortage of AI ambition on the part of insurers, but whether legacy technology can support the governance, auditability and regulatory controls regulators expect.
Agentic AI changes the regulatory risk equation
Traditional automation follows predefined instructions. Agentic AI, however, can interpret context, select actions, and execute work across systems.
This makes governance essential: If an AI agent touches a claim in five different systems before deciding what should happen next, an insurer needs far more than a final answer. It needs reliable data, permissions, applicable business rules, monitoring, traceability, and clear boundaries around what the agent is allowed to do.
But not only that, regulations can vary greatly by geography:
The U.S. regulatory environment varies state by state. California and Colorado have already added automated-decision requirements, while New York regulators continue tying AI-related risks to existing cybersecurity expectations. Regulators are also focusing on data provenance, testing, human review, consumer notices, vendor controls, and records sufficient to demonstrate compliance.
For international P&C insurers, Europe adds another layer. The EU AI Act sits alongside existing requirements under Solvency II, DORA, the Insurance Distribution Directive, and GDPR, bringing governance, fairness, cybersecurity, transparency, and oversight requirements into the picture.
In this case, AI agents don’t simply need instructions on what to do, they need built-in guardrails on what they’re allowed to do for this customer, in this jurisdiction, using this data, and under this specific policy type.
Legacy technology makes governance harder
Because many insurers still run operations across legacy and modern legacy systems with hard-coded integrations, fragmented data, and limited real-time interoperability, this makes the type of governance required by regulatory bodies difficult to orchestrate.
For example, an AI agent sitting on top of fragmented systems doesn’t make the fragmentation disappear — it just inherits that fragmentation into how well (or poorly) it’s able to operate.
If the agent can’t reliably determine which customer record is current, which jurisdictional rule applies, whether it has permission to access a field, or whether another system has already changed the claim, that agent’s autonomy becomes a liability pretty quickly.
EIS OneSuiteTM powered by CoreGenticTM, on the other hand, provides a cloud-native architecture designed around microservices, open APIs, event-driven processing, and data fluidity. This empowers AI agents to work with the most up-to-date, truthful data, eliminate operational silos, and have the kind of tracking and governance regulatory bodies require.
Put compliance where the work happens
In fact, EIS OneSuite powered by CoreGentic is built around that requirement: putting agentic AI inside the insurance core system, rather than layering it over disconnected systems and hoping the integrations, data, and controls hold together.
EIS brings agentic orchestration into the environment where the insurer’s business rules, workflows, permissions, customer and policy data, and operational controls already live. AI agents can work from governed insurance context instead of trying to reconstruct it across a collection of legacy applications.
The underlying EIS architecture provides configurable business rules, workflows, user permissions, event-driven processing, and Business Activity Monitoring (BAM). BAM maintains activity records tied to entities such as quotes, policies, billing, and claims, creating detailed operational history that supports auditability and accountability.
EIS also embeds regulatory requirements into the product-definition layer. Business teams can configure and future-date jurisdiction-specific rules, limitations, and eligibility criteria, allowing those rules to be enforced throughout quoting and policy administration rather than relying on an AI agent (or a person) to remember them.
This gives CoreGentic something far more useful than a prompt telling an agent to “be compliant” — it gives the agent governed access to the rules, permissions, data, workflows, and limits that determine what it can actually do.
EIS has backed this approach with our own AI governance. We became the first insurance core system to obtain ISO/IEC 42001 certification for AI management systems, covering AI governance, accountability, risk management, and responsible AI practices.
Navigate the Future of P&C Agentic AI with Confidence
If you’re ready to ensure your AI initiatives meet the evolving regulatory landscape, book a call with us to learn how EIS can help your P&C insurance organization navigate agentic AI compliance with confidence.
Our cloud-native architecture and CoreGentic setup empower you to embed governance directly into your operations, ensuring your AI agents work within the secure, auditable boundaries that modern regulators demand.
