As the use of agentic AI systems accelerates across industries, particularly in healthcare, it becomes essential to understand the infrastructure and protocols that enable scalable, modular, and interoperable agent-based ecosystems. Two key enablers in this space are the Model Context Protocol (MCP) and the Agent2Agent (A2A) protocol. While each serves a distinct purpose, their complementary roles are especially vital in complex enterprise environments where interoperability and flexibility are critical.

What is agentic AI?

Agentic AI refers to AI systems usually powered by large language models (LLMs) that can take actions on their own by using software tools or services. These systems don’t just provide answers or suggestions; they can actually complete tasks by interacting with other systems, such as scheduling appointments, retrieving data, or sending messages.

For example, a patient scheduling agent can check doctor availability and book appointments without human help. A clinical support agent might gather lab results or review patient history to assist with diagnosis (like the medications the patients are on, allergies, etc). These AI agents can figure out which steps to take and use the right tools or services to get the job done.

An AI model becomes an agent when it can not only “think” through a problem but also take the necessary steps to solve it by interacting with external systems.

MCP (Model Context Protocol): Enabling service interoperability

MCP is an open protocol that standardizes how services expose structured context and capabilities to agents. It acts as a universal interface between LLM agents and enterprise services such as APIs, databases, or internal proprietary tools enabling seamless interoperability across heterogeneous systems.

Why MCP matters in healthcare

Imagine a clinical support agent in a hospital attempting to access various services: an EMR system via REST, a diagnostic engine over gRPC, and a medical imaging archive using GraphQL. Without MCP, this agent would require custom logic to interface with each protocol.

With MCP, these services register their capabilities via MCP Servers, allowing any compatible agent to interact with them through a standardized JSON-RPC interface regardless of the service’s internal protocol.

Benefits of MCP in healthcare:

  • Service discovery: A benefits verification agent can dynamically query payor systems to understand what services are available (e.g., eligibility checks, co-pay estimation).
  • Protocol abstraction: A patient scheduling agent doesn’t need to care whether an appointment service uses REST or gRPC, it only speaks MCP.
  • Enterprise decoupling: Services can evolve independently. For instance, a legacy appointment system moving from SOAP to GraphQL would not require any change on the agent side if exposed via MCP.

A2A protocol: enabling agent-to-agent collaboration

Where MCP enables agents to call services, the agent-to-agent (A2A) protocol enables agents to call other agents. This is especially powerful in scenarios requiring multi-agent collaboration, negotiation, or delegation.

Key capabilities of A2A:

  • Agent capability discovery: Agents publish “agent cards” describing their skills. A scheduling agent can discover a language translation agent to help with multilingual patient coordination.
  • Bidirectional communication: Agents can initiate and respond to each other. For example, a clinical AI agent may delegate benefits verification to a specialized agent.
  • Recursive task delegation: Agent B, upon receiving a task from Agent A, may consult Agent C and return a composite result to Agent A.

Real-world healthcare use case

A primary care agent may need to refer a patient for a specialist appointment. If it lacks direct access, it might:

  1. Call a scheduling aggregator agent using A2A to request help.
  2. The aggregator agent may know that a dermatology scheduling agent has real-time availability data.

The task is either delegated, or the Primary Care Agent is directed to call the appropriate agent directly very similar to the chain of responsibility design pattern.

Comparing MCP and A2A

Aspect MCP A2A
Communication type Agent → service Agent ↔ agent
Initiation model Agent-initiated only Bidirectional
Primary use case Service interoperability Multi-agent collaboration
Discovery mechanism Service capability metadata Agent card with advertised Skills
Typical applications Calling APIs, databases Delegation, negotiation, brokering

Why both MCP and A2A are essential in enterprise healthcare

In healthcare enterprises, both protocols are indispensable:

  • MCP simplifies and standardizes how agents interact with backend services critical for accessing EMRs, claims systems, or scheduling APIs.
  • A2A allows for agent ecosystems where AI agents specialized in diagnosis, scheduling, billing, and translation can collaborate autonomously.

Can one replace the other? Not entirely.

While it’s possible to expose one agent to another via MCP (by treating the agent as a service), this limits communication to one-way, agent-initiated interactions. Full conversational workflows, which contain multiple steps in patient care like benefits verification, prior authorization, scheduling, and so forth, require the two-way communication that A2A provides.

MCP deployment in enterprise microservices

Deploying MCP across microservices in healthcare systems, each potentially handling protected health information (PHI) must be done with scalability and compliance in mind.

Recommended strategies:

  1. Embedded MCP Endpoints: Each service (e.g., Appointment Service) exposes /mcp/capabilities alongside existing endpoints. This ensures local ownership and alignment with business logic.
  2. API Gateway Integration: Central gateways (e.g., Apigee, Kong) act as proxy MCP servers translating JSON-RPC to REST/gRPC.
  3. Service Mesh Enhancements: In environments using Istio or Linkerd, mesh interceptors can handle MCP traffic at the network layer.

Benefits:

  • Reduced overhead: Avoids the need for separate MCP infrastructure for every service.
  • Unified access: Promotes consistency in access control, rate limiting, and observability.
  • Scalable governance: Central teams can enforce policies without service-specific configurations.

Building composable, scalable agentic AI in healthcare

As AI agents become key participants in healthcare workflows, from benefits verification to treatment follow up and so forth, their ability to interoperate across systems and collaborate with one another is paramount. MCP and A2A provide the foundational protocols to make this possible.

By deploying MCP for service abstraction and A2A for agent collaboration, healthcare organizations can unlock modular, composable AI systems that are easier to govern, scale, and evolve without sacrificing flexibility or interoperability.