If you're evaluating agentic AI vendors, you need to understand how the architecture actually works.
This page explains the reference architecture for healthcare agentic AI systems. Five layers. Each serves a specific purpose. All five required.
The Five-Layer Architecture
Each layer addresses a specific requirement healthcare organizations have. Remove any layer, and the system fails compliance, safety, or operational requirements.
Layer 1
Layer 2
Layer 3
Layer 4
Layer 5
The Problem
Healthcare workflows are complex. A prior authorization requires retrieving patient data, analyzing payer policies, generating documentation, getting human approval, submitting to portals, and tracking status. You need a system that can plan this sequence and coordinate execution.
The Solution : Multi-Agent Orchestration
Instead of one massive AI trying to handle everything, the orchestration layer uses specialized agents coordinated by a supervisor agent that plans overall workflow, routes tasks to specialists, and handles exceptions.
Specialized Sub-Agents:
Patient Data Agent → Retrieves and synthesizes clinical information
Policy Agent → Looks up payer-specific requirements
Documentation Agent → Generates medical necessity letters
Submission Agent → Interfaces with payer portals
Monitoring Agent → Tracks status and handles responses
Why This Matters:
Single-agent systems become brittle as complexity increases. When one agent tries to handle chart review, policy interpretation, documentation generation, and portal submission, performance degrades and failures are hard to diagnose.
Multi-agent architecture gives you modularity. When payer policies change, you update the Policy Agent. When documentation requirements change, you update the Documentation Agent. You don't rebuild the entire system.
Layer 2 – Knowledge:
How Agents Access Your Data
The Problem
AI models trained on generic medical data don't know your patient's history, your payer contracts, your institutional protocols, or your historical prior authorization patterns. Even worse, they can't interact with your systems to retrieve records, check eligibility, or trigger workflows. You need agents grounded in your actual data and equipped to act on it.
The Solution : Retrieval-Augmented Generation (RAG) + Function Calling
This layer gives agents two essential capabilities:
Retrieval-Augmented Generation (RAG) connects agents to your enterprise knowledge in real-time. When an agent needs information, it retrieves it from your systems before generating responses.
Function and Tool Calling enables agents to take action: query databases, check real-time eligibility, call APIs, trigger notifications, or write back to systems.
Together, these capabilities transform agents from text generators into systems that reason over your data and execute workflows.
Your Knowledge Sources:
EHR Data → Patient records, clinical notes, labs, imaging, medications
Payer Policies → Medical necessity criteria, coverage requirements
Institutional Protocols → Your formularies, care pathways, documentation standards
Historical Data → Prior successful authorizations, denial patterns, appeal strategies
Why This Matters:
Healthcare AI without RAG is guessing. Healthcare AI without function calling is trapped in read-only mode.
RAG ensures agents work with your actual data. When payer policies change, agents access updated policies immediately. When clinical guidelines evolve, agents reference current standards. No retraining required.
Function calling ensures agents can act on that data. Check eligibility in real-time. Route documents to the right reviewer. Trigger alerts when thresholds are crossed. Execute workflows end-to-end.
Together, they make agents useful in production healthcare environments.
Example: Prior Authorization for Specialty Medication
Agent processes prior authorization request for patient with rheumatoid arthritis requiring Humira.
The Problem
Your workflows span EHRs, payer portals, lab systems, imaging platforms, scheduling software. Agents need to read from and write to these systems. Manual integration for each system and each workflow is expensive and brittle.
The Solution : Tool Calling & Model Context Protocol (MCP)
Agents execute actions by calling tools—functions that interact with your systems. MCP standardizes how tools are defined, secured, and invoked.
Tool Examples:
Why This Matters:
Healthcare has dozens of disconnected systems. Tool calling eliminates manual system-hopping and data entry. MCP makes integration maintainable as your systems evolve.
Without MCP, you build custom integrations for every tool. With MCP, you define tools once using a standard protocol. Agents discover them dynamically. Adding new tools doesn't require re-engineering agents.
Layer 4 – Governance:
How You Maintain Control
The Problem
Healthcare AI systems access sensitive patient data, influence clinical workflows, and generate documentation affecting care decisions. You need oversight mechanisms, safety controls, and audit trails meeting regulatory requirements.
The Solution : Governance Layer with Four Components
Why This Matters:
Healthcare organizations are accountable for AI system actions under HIPAA, Joint Commission standards, and medical practice laws. Governance isn't optional. It's how you deploy AI with confidence in safety, compliance, and institutional control.
The Problem
AI systems in production consume resources (compute, API calls, staff time) and produce outputs of varying quality. Without visibility, you can't measure ROI, identify issues, or optimize performance. You're flying blind.
The Solution : Observability Platform
Real-time monitoring of agent behavior, costs, quality, and operational impact with dashboards, alerts, and analytics.
Why This Matters:
You can't justify AI investment to leadership without clear ROI data. You can't optimize performance without understanding bottlenecks. You can't catch quality issues before they affect patients without monitoring.
Observability transforms AI from a black box into a managed operational system with clear metrics, continuous improvement, and data-driven decision-making.
Why Scalefresh's Reference Architecture
We built this architecture specifically for healthcare operational workflows after working with multiple health systems. Scalefresh’s reference architecture provides a repeatable foundation for deploying agentic AI safely across healthcare workflows—without locking organizations into brittle, one-off implementations.