Why MCP could become the API layer for AI in Insurance

For years, one of the most discussed topics in insurance technology has been the concept of the “ecosystem”. In practice, ecosystem mostly meant one thing: enabling different actors, and therefore different systems, to interact with each other.

Insurers needed to connect distributors, aggregators, bancassurance channels, third-party services, claims networks and external platforms. And the technological answer to that need became progressively clear: APIs.

APIs became the standard way to expose capabilities, integrate applications and orchestrate interactions across systems.

That was the right abstraction layer for a world where applications were interacting with other applications: mostly stateless interactions based on predefined flows and deterministic journeys.

Yet, it may not be the right abstraction layer for AI agents, who are changing the nature of that interaction because they do not behave like traditional applications. They do not follow predefined flows, but they reason dynamically, combine capabilities, adapt interactions and make decisions based on context.

This changes the architectural problem quite significantly.

An API tells you which function can be called. But AI agents need more than functions, they need operational semantics: understanding under which conditions a function can be executed, what rules apply, what controls are required and which business constraints must be respected.

Especially in insurance market, this is not a secondary issue at all.

Every operation sits inside a framework of rules: product behaviors, underwriting logic, authorization layers, compliance requirements, traceability obligations. And the industry already learned years ago that integration without governance creates complexity faster than it creates value.

A core insurance platform needs to expose the operational context. It needs to make clear which capabilities exist, how they can be combined, which rules govern them, which permissions apply and how workflows are orchestrated.

The nature of AI agents is also evolving: we are moving beyond standalone assistants toward multi-agent environments, where different AI systems interact within the same process. Customer-facing assistants, intermediary tools and internal support systems are no longer isolated. They collaborate, exchange information and trigger actions across the value chain.

This shift changes entirely the scale of the problem, because one chatbot answering questions is one thing, but multiple agents interacting simultaneously with policy administration, pricing or claims processes is a completely different challenge.

Just exposing APIs to enable a single capability is no longer enough. This is about coordinating many and coordination, especially in insurance, means structure.

This is why at RGI we believe core systems are becoming even more strategic than ever: when multiple AI agents become autonomous, somebody still needs to guarantee consistency, transactional integrity, compliance, rule enforcement, traceability and resilience at scale. In terms of logic, this is the core system, the environment where all interactions are orchestrated, controlled and made scalable.

And this is also where the Model Context Protocol (MCP) deserves attention.

By standardising how AI agents discover capabilities, exchange context and interact with enterprise systems, MCP introduces the possibility of reducing fragmentation and enabling interoperability.

In simple terms, it provides a common language between AI and the platforms that support it.

This is where enterprise architectures are heading, especially in industries like insurance where reliability, governance and control are fundamental operational requirements. And this is also where the ability to manage AI interactions, along with human and application ones, becomes a key differentiator.

So, if the question is “how do we make AI agents operate safely and consistently inside insurance processes?”, our answer is “making them operate within the core system, just as human users and external applications already do”.

In many ways, MCP may become for AI agents what APIs became for applications: a standard way to access capabilities, interact with processes and orchestrate operations at scale.

And for insurers, this could open a completely new interaction and distribution layer, enabling AI-driven customer experiences, new service models and potentially even new sales and distribution channels connected directly to core operations.

This is why at RGI we are evolving our core platforms to speak the language of AI agents: exposing capabilities, context and governance models in a way that AI-driven interactions can become scalable, controlled and consistent.