New workflow integration methods will define agentic AI success
The development of new AI agents will continue to accelerate in the coming years, with each designed to collaborate in solving some of healthcare’s most persistent and long-standing challenges. To keep pace, these agents must be able to integrate with trusted tools, resources, and databases in a way that scales as quickly as the agents themselves. Traditional integration methods, however, rely on slow, custom-built workflows that cannot meet the speed or flexibility this new environment demands.
Traditionally, integrating clinical data meant building direct API connections that demanded heavy engineering effort to maintain. Introducing autonomous agents into this mix multiplies the complexity when numerous agents in a single workflow need consistent, secure access to trusted clinical content. Without a standardized way to connect, every new agent adds friction, risk, and cost.
Agents need to “know” how to ask for information and how to interpret the answer. Without a standardized framework, developers must build custom integrations for every single tool an agent needs to use. If underlying data sources change, it can demand reworking or introduce instability.
What is Model Context Protocol (MCP) and how does it work?
The Model Context Protocol acts as a standardized integration layer between AI agents and external tools. It is not an AI model itself, nor is it a database. Instead, think of it as a universal translator and traffic controller combined.
MCP provides a consistent interface for agents to discover and use specific functions or “tools.” When an agent needs to look up a medication or screen for drug interactions, it sends a request through the MCP server. The server retrieves the specific, contextually relevant content from trusted back-end systems (like clinical APIs) and delivers it back to the agent in a structured format the AI can easily parse.
This architecture offers several distinct advantages over traditional integration methods:
Standardization for faster deployment: MCP eliminates the need for custom, one-off integrations by providing a universal standard for connecting AI agents to trusted tools. This means faster implementation and reduced engineering overhead.
Governance for safety and compliance: MCP helps enforce clearer boundaries around what an agent can access and how responses are structured, reducing the risk of misuse when combined with appropriate governance and oversight.
Scalability for multi-agent workflows: MCP turns core clinical data services into reusable tools that can be deployed across multiple AI agents and use cases. Once the MCP layer is in place, adding new agents or expanding capabilities becomes seamless without rebuilding integrations from scratch.
Practical applications of MCP across healthcare
By standardizing how agents connect to key clinical resources, MCP delivers secure, reliable, and consistent access to the information needed for modern workflows, including agentic AI. This approach accelerates innovation, supports compliant data exchange, and empowers teams to focus on developing solutions that drive better health outcomes.