🏥 Institute for Responsible Healthcare AI • BeResponsibleAI Educational Initiative • Conceptual Reference Model

Author: Dr. Sharad Maheshwari MD • imagingsimplified@gmail.com

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🏛️ Institute for Responsible Healthcare AI

Enterprise Agentic AI Reference Architecture

Reference Blueprint v3.2

🏗️The 5 Core Execution Layers & Cross-Cutting Control Planes

Institute Reference Model

Production enterprise agentic AI systems require decoupling core operational capabilities from security and governance. Governance and observability are not terminal layers; they are cross-cutting control planes that continuously constrain every user prompt, model gateway call, tool execution, state mutation, and output rendering.

Core Execution Planes 🛡️ Enveloped by Cross-Cutting Control Planes

1

Experience & Interaction Layer

Chat UI, Embedded Workflow Canvas, Approval Interfaces, Artifact Rendering

User Interface

2

Agent Runtime & Orchestration Layer

Planning, Dynamic Routing, Workflow Graphs, Checkpointing, Retry Loops

Control Engine

3

Model Services Layer

Policy Gateways, Multimodal LLMs/VLMs, Task Vision Models, Embeddings, Rerankers

Cognition Engine

4

Knowledge & State Services Layer

RAG Indexes, Session State, Durable Tasks, Semantic Memory, Provenance

Context & Memory

5

Tool & Integration Layer

MCP Servers, A2A Endpoints, DB Connectors, Sandboxes, Systems of Record

Capabilities

🛡️ Cross-Cutting Control Planes: IAM • Consent • Security Controls • Tamper-Evident Audit • Tracing

Spans All Layers

Layer Metric & Trade-off Profile

🏷️Enterprise Technology & Taxonomy Matrix

Ecosystem clarity requires separating standardized protocols from orchestration runtimes, retrieval engines, and underlying systems of record.

💡Architectural Responsibility Quick Solver

Ecosystem Component Classification

Filter Taxonomy: All Ecosystem Components Workflow & Runtime Orchestrators Retrieval & Context Engines Agent-to-Tool Protocols (MCP) Inter-Agent Protocols (A2A) Systems of Record & Access Standards Cross-Cutting Control Planes

Component Name	Taxonomy Classification	Primary Architectural Question Answered	Core Responsibilities & Capabilities	Related Implementations / Alternatives

🔌Integration Strategies: Native Direct Tools vs. MCP Protocol

Architects face a pivotal design decision when exposing capabilities to agents: Native tools optimize simplicity and invocation overhead; MCP optimizes reuse, isolation, and independent lifecycle management.

🏥Recommended Regulated & Clinical Operational Pattern

Native Loop Orchestrator ⇉ Policy & IAM Gateway ⇉ Governed MCP Service ⇉ Audited PACS / RIS / FHIR Write-Back

Architectural Trade-off Radar

Scale: 1 (Lowest) to 5 (Highest)

Qualitative ratings comparing native in-process tool calls against decoupled MCP server microservices.

*Note: Latency, security, and operational complexity depend on transport selection, authorization gates, and implementation.

🎛️Deployment Integration Recommender

Select options

Toggle specific deployment criteria below to evaluate whether Native Tools, MCP Protocol Servers, or a Hybrid Architecture is recommended:

Capabilities shared across multiple agent applications / teams? Requires process isolation, distinct IAM, rate limits & central audit? Requires lowest invocation overhead for local loop execution? High-risk healthcare/clinical systems (PACS/RIS/FHIR)?

Select criteria above to generate an enterprise integration recommendation.

Architectural Dimension	Native In-Process Tools	MCP Protocol Servers
Coupling & Portability	Tightly coupled to orchestrator process	Decoupled & framework-agnostic protocol boundary
Latency & Overhead	Lowest invocation overhead	Network transport overhead with payload serialization
Security & Isolation	Shares process memory by default	Isolated process, separate credentials, IAM, and sandboxing
Infrastructure Complexity	Zero additional network servers required	Requires server runtime, monitoring, and deployment lifecycle

🤝Protocol Disambiguation: MCP vs. A2A vs. Communication Mechanisms

It is critical to distinguish higher-level agentic protocols from underlying communication and transport mechanisms. MCP standardizes how AI applications discover and interact with external capabilities, whereas A2A standardizes how autonomous agents collaborate and delegate tasks.

🔌Agent-to-Tool Protocol (MCP) App ⇉ Tool

MCP standardizes how AI applications discover and interact with external tools, resources, and prompts. Resources provide application-controlled context, while tools expose model-invokable operations.

🌐Agent-to-Agent Protocol (A2A) Agent ⇉ Agent

Task-oriented agent communication, capability discovery (Agent Cards), status updates, and artifact exchange; tamper-evident history is provided by the surrounding audit architecture.

⚡Communication & Transport Low-Level Wire

HTTP-based APIs, REST, JSON-RPC, gRPC, Server-Sent Events, and WebSockets can carry or support protocol messages across systems.

Governed Multi-Agent Execution Flow Simulator

Walk through a governed workflow showing context retrieval, inter-agent delegation, and pre-mutation safety gates

🛡️ Governed Mutation Safeguards: Identity Scope Check • Idempotency Keys • Pre-Commit Inter-Agent Feasibility • Human Approval Gate • Tamper-Evident Audit

1. Intent & Identity 1

"Can I reserve transport with specialized luggage handling?"

Runtime Agent verifies OAuth2 IAM scope & InvocationContext

2. MCP Policy Search 2

MCP tool search_policies executed over transport connection.

Managed Vector Search (<=> Cosine Distance)

3. A2A Delegation 3

Primary Agent delegates feasibility check to specialized Logistics Agent via A2A.

A2A Task submitted: Logistics Agent confirms capacity

4. Governed Write-Back 4

MCP tool book_reservation executes state-changing operation after user confirmation.

Idempotency Key validated & Audited write committed

[SYSTEM READY]: Click steps 1-4 above to simulate execution path...

🏥 Institute Policy Notice: Conceptual educational framework published by the Institute for Responsible Healthcare AI; implementation requires institutional review, regulatory evaluation, and clinical validation.

Governance Model

🏥Clinical & Healthcare Reference Architecture (PACS/VNA, RIS, EHR & FHIR)

High-Risk Implementation

In clinical medical and imaging environments (authored by Dr. Sharad Maheshwari MD at the Institute for Responsible Healthcare AI), PACS/VNA, RIS, and EHR systems are not generic external tools—they are regulated high-risk systems of record subject to strict clinical, privacy, authorization, and operational controls.

🔄Governed Clinical Execution Flow

End-to-End Clinical Lifecycle

1. Identity User Credential & Role

2. Context Patient & Encounter ID

3. Auth Check System Governance

4. Runtime Agent Invocation

5. Min. Read DICOMweb / FHIR Read

6. Evidence Review Panel Context

7. Human Gate MD Approval & Sign-Off

8. Write-Back Audited RIS/EHR Write

🔐System Authorization & Access Boundaries

PACS/VNA, RIS, and EHR systems require system-specific identity, authorization, audit, and data-governance controls. SMART-on-FHIR/OAuth2 commonly governs EHR/FHIR access, while DICOMweb access is governed through the imaging platform's authentication, authorization, and audit mechanisms.

🖼️PACS / VNA & DICOM Attribute Handling

De-identification must address DICOM attributes, private tags, burned-in pixel annotations, overlays, structured content, and OCR-detectable text, with validation appropriate to the intended secondary use.

📜Auditable Source Provenance & Evidence Panel

The system should retain auditable provenance linking generated content to source data, prior reports, guidelines, model/version metadata, and relevant image series or key images. The reporting interface may expose this evidence through a contextual review panel.

👨‍⚕️Mandatory Human Approval & Governed Write-Back

Agents may retrieve data and generate drafts. Any write-capable operation must be mediated by a policy-controlled service and require authenticated, credentialed clinician approval; the agent must not autonomously finalize reports, submit orders, or alter the legal medical record.

🛡️Clinical Safety & Governance Controls

Mandatory Guardrails

🎯Purpose Limitation

Care delivery, quality assurance, research, or education must be explicitly declared before invocation.

🔍Minimum Necessary Access

Retrieve only the studies, prior reports, labs, and notes strictly required for the stated task.

🚨Break-Glass Handling

Emergency-access rules, mandatory justification capture, and real-time enhanced audit review.

🪪Patient Matching Safeguards

Accession number, study UID, patient ID, and encounter consistency checks before retrieval or write-back.

🧬Model Lifecycle Controls

Versioning, validation population checks, performance monitoring, drift detection, rollback, and incident management.

⚠️Automation Bias Safeguards

Drafts labeled non-final, explicit display of uncertainty/limitations, preventing silent report acceptance.

⛔Fail-Closed Failure Mode

If authorization, patient-context validation, provenance, or evidence retrieval fails, the system must fail closed or downgrade to a non-clinical drafting mode without mutating systems of record.

🔬Clinical Radiology Agentic Workflow Example

1. PACS Trigger & DICOMweb A DICOMweb service retrieves minimum necessary study metadata under authenticated clinical context; de-identification is applied if data leave the care environment.

2. Scoped Context Retrieval Under patient- and encounter-scoped authorization, a governed service retrieves relevant priors, prior reports, indication, and key lab values (e.g., eGFR).

3. VLM Finding Draft Multimodal VLM drafts preliminary findings and impressions with key image series references, exposing source evidence through a contextual review panel.

4. Physician Approval Gate Dr. Maheshwari reviews, edits, and approves before an audited write-back service updates the RIS/EHR record.

🛠️Enterprise Reference Stack Architect Configurator

Decision Engine v3.2

Configure project variables below to generate an interactive conceptual reference architecture blueprint. The decision engine evaluates domain sensitivity, state statefulness, write capabilities, data residency, and delegation bounds to output dynamic architectural rationales, granular model tier roles, governed MCP specifications, and structured 5-pillar governance controls.

Architecture Project Selectors

6 Criteria Evaluated

1. Implementation Domain: Domain Risk Healthcare & Clinical Imaging (PACS/RIS/FHIR) Regulated Financial & Audit Log Heavy Standard Enterprise / SaaS Application

2. Primary Orchestration Logic: Runtime Control Cyclic State Machine / Graph Workflows (LangGraph/ADK) Heavy Document Indexing & Context Retrieval (LlamaIndex) Linear Sequential Pipelines

3. Tool Connectivity & Access: Integration Pattern Decoupled Governed MCP Protocol Microservices Hybrid (Native Local Loop + Governed MCP Shared Tools) In-Process Framework Native Functions

4. Operation & Mutability Mode: State Mutation Risk State-Mutating Operations (Writes, Orders, Clinical Sign-Off) Read-Only Information Retrieval & Draft Generation

5. Deployment & Data Residency: Infrastructure On-Premises / Air-Gapped Infrastructure Hybrid Enterprise VPC + Internal Systems Gateway Multi-Tenant Managed Cloud / SaaS

6. Inter-Agent Delegation Scope: Coordination Scope Cross-Department / Independent Multi-Agent System (A2A Protocol) Single Application / Embedded Graph Subgraph

⚡Generated Reference Architecture Recommended Conceptual Blueprint

Designed to reflect the Institute for Responsible Healthcare AI principles; implementation requires institutional review and validation.

Institute for Responsible Healthcare AI • BeResponsibleAI Educational Initiative • Enterprise Agentic AI Reference Architecture

Authored by Dr. Sharad Maheshwari MD (imagingsimplified@gmail.com)