Agentic AI Arrives: What Healthcare Organizations Should Know Before Choosing a Platform

In early October, Microsoft, Google, OpenAI, and AWS each introduced new agentic AI platforms—and Databricks recently joined them. These frameworks promise to reshape how work gets done. For healthcare, the opportunity is huge, but so is the need for the right strategy and governance.

A new wave of agentic AI

This week, four of the biggest tech companies—Microsoft, Google, OpenAI, and AWS—each unveiled new platforms for building and deploying agentic AI. These updates mark a clear shift from AI as a helper to AI as an active participant in work.

Databricks joined the trend earlier this fall with Agent Bricks, a framework designed to optimize production-grade AI agents on enterprise data.

For healthcare organizations, this marks a turning point. Every major cloud and AI provider now offers tools to build and deploy intelligent agents. The question isn’t if to use agentic AI—it’s which platform fits best given your data, compliance, and infrastructure.

From copilots to collaborators

Until recently, most AI systems were copilots—tools that summarized information or answered questions. Agentic AI takes it further. These agents can reason over data, plan multi-step actions, and connect to systems to perform work.

In healthcare, that could mean:

• Reviewing documentation and initiating prior authorizations
• Updating care plans
• Drafting member outreach messages
• Extracting structured data from clinical notes

The potential is clear. The challenge is choosing technology that can do this safely, securely, and within the rules that define healthcare.

The major agentic platforms at a glance

Microsoft Agent Framework

Microsoft combined its Semantic Kernel and AutoGen projects into one open-source foundation. It supports .NET and Python developers, enables multi-agent orchestration, and integrates deeply with Azure and Microsoft 365.

Best fit: Health systems and payers already running on Azure that need mature enterprise governance and compliance controls.

Google Gemini for Enterprise

Google’s Gemini Enterprise extends Gemini models into a workplace platform for building and managing agents. It integrates with Workspace, Salesforce, SAP, and BigQuery, offering strong controls for compliance, data residency, and security.

Best fit: Large healthcare organizations that want to scale AI across roles while maintaining centralized governance.

OpenAI AgentKit

AgentKit provides a developer toolkit to design and embed agents using visual tools like Agent Builder and ChatKit. It also includes evaluation features for measuring model performance, cost, and reliability.

Best fit: Innovation teams or startups building healthcare agents that need flexibility and fast iteration.

AWS Agentic AI Quick Suite

AWS’s new Agentic AI Quick Suite combines Quick Index, Quick Research, Quick Flows, and Quick Automate to connect 50+ data sources and 1,000+ third-party apps via the Model Context Protocol. It inherits AWS’s enterprise security and HIPAA-ready compliance.

Best fit: Healthcare organizations already using AWS that want to automate workflows and connect systems securely.

Databricks Agent Bricks

Agent Bricks, launched earlier in the fall, focuses on production-quality AI agents built directly on enterprise data. It supports synthetic data generation, benchmarking, and continuous evaluation for cost and accuracy.

Best fit: Healthcare data teams already using Databricks for analytics and governance.

How healthcare should evaluate the options

Choosing among these platforms isn’t about who has the flashiest launch. It’s about which one aligns with your infrastructure, compliance framework, and data maturity.

1. Data access and interoperability

Agents rely on clean, secure, accessible data. Look for frameworks that connect easily to EHRs, claims systems, CRMs, and analytics tools using FHIR and HL7 standards.

• Microsoft, AWS, and Databricks align well with existing enterprise data layers.
• Google’s Gemini integrates closely with BigQuery and Workspace.
• OpenAI requires custom connectors but offers flexibility for hybrid setups.

2. Security and compliance

For healthcare, HIPAA compliance, PHI protection, and data residency come first.

• Microsoft and Google lead in enterprise-grade compliance.
• AWS has long been a foundation for HIPAA workloads.
• Databricks and OpenAI can be compliant but depend on customer configuration.

3. Governance and visibility

You need to know what agents are running, what data they touch, and how decisions are made.

• Google and Microsoft excel in centralized governance and audit control.
• Databricks emphasizes traceability and performance tracking.
• AWS supports fine-grained access and policy management.
• OpenAI prioritizes evaluation transparency for developers.

4. Measurement and ROI

AI projects need measurable outcomes.

• OpenAI and Databricks offer strong evaluation tools.
• Microsoft, AWS, and Google provide telemetry and analytics integration.

The key is connecting performance metrics to business outcomes—time saved, accuracy gained, or costs reduced.

5. Fit with your IT environment

The right choice usually matches your current cloud strategy:

• Azure-first: Microsoft Agent Framework
• GCP-first: Gemini Enterprise
• AWS-first: Agentic AI Quick Suite
• Databricks user: Agent Bricks
• Rapid prototyping: OpenAI AgentKit

Healthcare needs its own layer

All these platforms are general-purpose. None understand healthcare data, terminology, or compliance out of the box. They need a layer that adds healthcare reasoning, PHI safeguards, and FHIR-native integration.

At Productive Edge, we call this the healthcare layer—and it’s what powers our Boost Health AI solution. Boost connects with any of these agentic frameworks to bring the data models, context, and governance healthcare requires. It doesn’t replace the major platforms—it makes them work safely and effectively for healthcare organizations.

A practical path forward

A good starting point doesn’t require a full overhaul.

1. Pick one high-friction workflow—claims intake, care coordination, or member support.
2. Choose the agentic platform that aligns with your IT and compliance stack.
3. Add a healthcare layer to handle reasoning, data standards, and PHI controls.
4. Measure results, refine, and scale.

This approach reduces risk while building capability and confidence over time.

The bottom line

October’s announcements confirm that agentic AI has arrived. Every major cloud and AI provider now has a framework for building intelligent agents that can act, decide, and learn within enterprise systems.

For healthcare, the opportunity is enormous—but success depends on more than technology. The organizations that will benefit most are those that:

• Anchor AI agents in secure, compliant data foundations
• Govern them responsibly
• Layer in healthcare expertise where it counts

Agentic AI won’t be won by one vendor. It’ll be built by the organizations that connect these ecosystems together—and make them work for healthcare’s real-world needs.