The conversation around AI in healthcare is evolving rapidly.

Beyond chatbots and copilots, there is a growing focus on AI agents - systems that do not just generate responses, but actively interact with data, tools, and workflows.

This raises a fundamental set of questions:

Why do we need AI agents in healthcare?
How do standards like FHIR, MCP, and A2A fit together?
What does this architecture actually enable in real-world workflows?

We recently explored these ideas in depth, including a walkthrough video. Below is a structured breakdown of the key concepts.

What Is an AI Agent?

An AI agent is more than a large language model.

At its core, it can be understood as:

LLM + Tools

This combination allows the system to move beyond generating text into taking action.

An agent can:

Read from an EHR
Query clinical guidelines or knowledge bases
Initiate workflows such as prior authorization
Retrieve and synthesize patient-specific context

All of this can be triggered through natural language.

Instead of navigating multiple systems manually, users can express intent, and the agent orchestrates the steps required to fulfill it.

Why Healthcare Needs Agents

Healthcare is defined by three characteristics:

Large volumes of data
Significant amounts of unstructured information
Non-deterministic, judgment-driven workflows

Traditional systems struggle to operate effectively across all three.

AI agents address this gap by:

Interpreting unstructured clinical data
Connecting across fragmented systems
Supporting complex decision-making processes

This makes them particularly valuable in use cases such as clinical decision support, prior authorization, patient summarization, and care coordination.

Agents are not just a new interface. They represent a new operational layer.

MCP: Enabling Agents to Act

Model Context Protocol is the standard that enables agents to interact with tools and external systems.

MCP wraps APIs and other capabilities into structured, discoverable interfaces that LLMs can understand and use.

Through MCP, an agent can:

Discover available tools
Decide when a tool is needed
Invoke the tool through the application layer
Incorporate the results into its reasoning

This transforms LLMs from static responders into systems that can operate within real workflows.

Without MCP, every integration would be custom and fragile. With MCP, interactions become standardized and scalable.

A2A: When Agents Work Together

As systems grow more complex, a single agent is often not sufficient.

This is where Agent-to-Agent (A2A) communication comes in.

A2A enables one agent to interact with another specialized agent. For example:

A clinical copilot agent consulting a clinical trial matching agent
A patient-facing agent interacting with a billing or scheduling agent

This approach provides several advantages:

Specialization across domains
Reduced load on any single model
Clear organizational and architectural boundaries

Instead of building one monolithic system, we move toward coordinated ecosystems of specialized agents.

Where FHIR Fits In

FHIR plays a foundational role in enabling agents within healthcare.

It provides structured, standardized access to clinical data.

FHIR supports agent workflows in multiple ways:

Structured data access through FHIR-based MCP servers
Patient context through SMART on FHIR authentication
Access to unstructured clinical data via DocumentReference resources

FHIR ensures that agents operate on consistent, interoperable data rather than fragmented or proprietary formats.

In many ways, FHIR is the data layer that makes agentic workflows viable in healthcare environments.

Toward Conversational Interoperability

Beyond individual standards, there is a broader vision emerging.

Conversational Interoperability, or COIN, represents the idea that interoperability will increasingly be mediated through AI-driven interactions rather than static integrations.

Instead of systems exchanging data in predefined workflows, agents interpret intent, retrieve context, and coordinate actions dynamically.

This represents a shift from integration-driven interoperability to intelligence-driven interoperability.

Watch the Full Breakdown

We have created a detailed walkthrough covering these concepts, including how MCP, FHIR, and A2A work together in real-world scenarios.

▶ Watch the video below to explore:

What AI agents are and how they differ from traditional AI apps
How MCP enables tool usage and orchestration
How A2A supports multi-agent collaboration
How FHIR provides the structured data foundation

Building the Next Generation of Healthcare AI

Understanding these concepts is just the starting point.

The real opportunity lies in building and testing real-world applications.

To support this, we are running Agents Assemble, a hands-on challenge focused on building healthcare agents using MCP, A2A, and FHIR.

Deadline: May 11, 2026
$25,000 in prizes
1000+ participants already registered
No coding required
Teams are welcome

This initiative is designed to bring together developers, clinicians, and innovators to experiment with the next generation of healthcare infrastructure.

Closing Thoughts

AI agents represent a shift from passive intelligence to active systems that can reason, act, and collaborate.

FHIR structures the data.
MCP enables action.
A2A enables collaboration.

Together, they form the foundation for a new generation of healthcare applications.

At Prompt Opinion, we are actively building and exploring this space. If you are working at the intersection of AI, healthcare, and interoperability, we would welcome the opportunity to connect and collaborate.

Why Healthcare Needs AI Agents: Connecting FHIR, MCP, and A2A