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.
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.
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:
The potential is clear. The challenge is choosing technology that can do this safely, securely, and within the rules that define healthcare.
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’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.
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’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.
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.
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.
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.
For healthcare, HIPAA compliance, PHI protection, and data residency come first.
You need to know what agents are running, what data they touch, and how decisions are made.
AI projects need measurable outcomes.
The key is connecting performance metrics to business outcomes—time saved, accuracy gained, or costs reduced.
The right choice usually matches your current cloud strategy:
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 good starting point doesn’t require a full overhaul.
This approach reduces risk while building capability and confidence over time.
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:
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.