LLMs, Agentic Patterns, and Practical Healthcare: Why Tools Matter (Part III)
TL;DR Large language models (LLMs) are powerful at reasoning and language but cannot perform real-world actions on their own; the agentic pattern—exposing callable tools or functions—is the practical solution that lets LLMs drive systems safely and reliably. Try DHTI — help us democratize GenAI.
Image credit: JPxG, Public domain, via Wikimedia Commons
LLMs excel at understanding, summarizing, and generating text, but they are not actuators: they cannot click buttons, run code, or update records by themselves. To bridge that gap, engineers use the agentic design pattern in which an LLM is paired with tools—well-defined functions that actually perform actions (search, execute code, call APIs, update databases). This pattern is widely discussed in recent engineering guides and industry posts about tool use for agents.
How the agentic pattern works
An agentic system exposes a catalog of tools (functions) with clear schemas. The LLM decides which tool to call and with what parameters; the tool executes the action and returns structured results the model can reason over. This separation keeps the model focused on decision-making while delegating side effects to auditable, testable code.
In healthcare, clinical calculators and scoring algorithms (e.g., eGFR, SOFA, CHADS-VASc) can be exposed as callable services so the model can compute and return validated numeric outputs rather than guessing formulas.
Standards that make agentic systems interoperable
Two emerging standards are central to scaling agentic AI: Model Context Protocol (MCP) and Agent2Agent (A2A). MCP is an open protocol designed to connect models to data sources and tools via a standardized server interface; it defines how tools are described, invoked, and secured so models can access files, APIs, and computations consistently. A2A is an open agent-to-agent communication protocol that enables different agents to coordinate, delegate, and stream messages in multi-agent ecosystems—think of it as the networking layer for autonomous agents.
MCP in healthcare: exposing calculators and models as tools
Medical calculators and algorithmic models can be packaged as MCP servers so any compliant model can call them with patient parameters and receive validated outputs. Open implementations and marketplaces already demonstrate medical-calculator MCP servers that expose dozens of clinical tools for integration into EMRs and workflows. Projects like AgentCare show how MCP servers can connect LLMs to EMRs (SMART on FHIR) to fetch vitals, labs, and run clinical workflows.
Practical barriers for clinicians
Despite the promise, non-technical clinicians face friction: setting up MCP servers, wiring tools into EMRs, and maintaining models and calculators requires engineering resources. Algorithms and models also evolve—guidelines change, new equations are published—so a one-time integration can become stale quickly.
Where DHTI fits
This is where DHTI steps in: by lowering the technical barrier, managing tool deployments, and keeping clinical algorithms up to date, DHTI helps healthcare teams adopt agentic GenAI without deep engineering overhead. In the next post, I will explain exactly how DHTI handles deployment, governance, and lifecycle updates.