Healthcare AI Agents. Assembled.

From fragmented pilots to production-ready workflows, powered by open standards

Agents Assemble Challenge is Live. Learn More →

Healthcare AI is everywhere. Production is rare.

Everyone is building agents. Few are turning them into workflows.

Fragmented agents. Isolated tools. Lost context.

Agents built by different teams can’t speak to each other.
Swapping a model means breaking and rebuilding the integration.
Patient context disappears across multi-agent workflows.
Compliance teams can’t trace what the AI did or why.

Prompt Opinion connects healthcare organizations with interoperable AI agents, tools, and workflow infrastructure built on MCP, A2A, and FHIR.

What is Prompt Opinion

Prompt Opinion is a healthcare AI assembly platform.

Builders publish interoperable agents and tools. Organizations connect their EHR, policies, and data. Prompt Opinion grounds them in the right context and assembles them into deployable workflows.

Builders create the intelligence.

Create agents and tools on open standards. Publish once and make them available to every organization on the platform.

Organizations supply the context.

Connect your EHR, data, and policies. Discover agents and assemble them into workflows grounded in your environment.

Standards make it interoperable.

MCP, A2A, and HL7 FHIR keep every agent portable across models, vendors, and organizations.

Platform assembles workflows.

Authentication, credential bridging, context routing, audit trails, and governance are handled once at the platform layer. Your team can stay focused on the workflow, not the plumbing.

What can Prompt Opinion do

See what is possible today with assembled agents.

These are not demos. They are agents already running on the platform, producing real deliverables inside real healthcare workflows. Each scenario combines agents, tools, and context in different ways. Together, they show what interoperable infrastructure makes possible in real healthcare workflows.

Prior Authorization

Context: Patient

Output: Complete prior auth packet with evidence extracted from the chart, payer form completed, submitted via API, and follow-up task created

Users: Care Teams, Revenue Cycle Management (RCM)

Clinical Trials Matching

Context: Patient

Output: Shortlist of eligible trials matched to a specific patient from unstructured notes and complex inclusion criteria.

Users: Clinicians, Research Teams

Clinical Decision Support

Context: Patient / Cohort

Output: Evidence-backed clinical brief grounded in your organization's guidelines.

Users: Clinicians, Clinical Leaders

Population Health Gaps

Context: Cohort

Output: Table of patients with open care gaps sorted by risk using structured and unstructured EHR data.

Users: Pop Health Managers, Operations Teams

Policies & Procedures

Context: General

Output: Policy-aligned guidance grounded in your organization's own documents.

Users: Compliance, Clinical Leadership

Patient Summarization

Context: Patient

Output: Longitudinal patient summary across chart history with flagged anomalies and care continuity gaps.

Users: Clinicians, Care Coordinators

How Agents Get Assembled

Four sources. Any combination. One platform.

Every workflow on Prompt Opinion combines agent logic with organizational context - your EHR, your policies, and your data. You can assemble these workflows from four sources.

01

Platform Templates

Start with templates

Pre-built agent templates for common healthcare workflows. Connect your data, configure the context, and deploy. No build required.

02

Native Custom Agents

Build natively

Create a custom agent directly inside the platform. Define the use case, connect your data sources, and set the context. The platform handles the infrastructure.

03

External Agents

Discover from the registry

Builders publish agents to the registry on open standards. Discover them, connect them to your context, and assemble them into your workflows without custom integration.

04

MCP Tools & Servers

Connect reusable tools

Reusable tools published by builders include FHIR data layers, clinical calculators, coding lookups, and clinical trial databases. Any agent can plug into any tool.

How Agents Become Context-Aware

Every agent knows where it’s working.

For AI to be useful in healthcare, it cannot behave the same way in every setting. Context determines what the agent sees, how it reasons, and what it produces. The same agent can produce very different outputs depending on the context in which it operates.

Patient Context

At the bedside

One patient, one chart. The agent focuses on individual clinical decisions - drafting prior authorizations, summarizing visits, flagging risks for this patient, right now.

Cohort Context

Across a population

Managing a panel or program. The agent shifts to group-level reasoning - finding care gaps, surfacing trends, and identifying patients who need follow-up.

Research Context

In the evidence

Reviewing protocols, matching trials, and evaluating guidelines. The agent focuses on criteria and clinical evidence that is verifiable and grounded.

Built on Open Standards

No lock-in. Any agent. Any model. Any source.

Prompt Opinion is built on three core interoperability protocols: MCP, A2A, and FHIR.

Open standards enable organizations to adopt, replace, and extend agents without rebuilding infrastructure or reworking their technology stack.

MCP

MCP

Model Context Protocol - Anthropic

The tools layer.
Reusable tools without bespoke integrations.

How agents discover and call external functions such as FHIR queries, coding lookups, scheduling, and document retrieval. Stateless and language-agnostic.
A2A

A2A

Agent-to-Agent Protocol - Google

The agents layer.
Multi-agent coordination across workflows.

How autonomous agents communicate, delegate, and collaborate across complex clinical tasks and organizational boundaries.
 HL7 FHIR

HL7 FHIR

Healthcare Data Standard

The healthcare data layer.
Clinical context grounded in standardized healthcare data.

The standard for how patient data is structured and accessed across EHRs, labs, and payers. The foundation for clinical context.

Built for Teams Creating and Deploying Healthcare AI

One platform for builders and organizations.

Prompt Opinion connects builders of interoperable agents with organizations ready to assemble and deploy them in real workflows.

Builders


Create and publish to the ecosystem

Builders include clinicians, informaticists, operators, and engineers, who turn healthcare workflow challenges into interoperable agents and tools.

Prompt Opinion gives you the infrastructure to build on open standards, publish once, and make your work available across the ecosystem.


What builders get

  • → Open standards infrastructure - no custom plumbing required
  • → Reference implementations in TypeScript and Python
  • → Publish once, reach every organization on the platform
  • → MCP for tools, A2A for agents

Organizations


Assemble and deploy in real workflows

You know the problems. You have the data. You need AI that works inside your clinical workflows - not another pilot that never makes it to production.

Prompt Opinion gives you the infrastructure to assemble agents grounded in your data, workflows, and operating context from day one.


What organizations get

  • → Agents connected to your EHR from day one
  • → Context-aware execution across patient, cohort, and research-driven workflows
  • → Discover from the registry or build your own
  • → Move from pilot to production without rebuilding infrastructure
  • → Governance, audit trails, and compliance built in

What Prompt Opinion Delivers

Five ways assembled agents turn AI into usable work.

Healthcare workflows do not run on insight alone. They depend on outputs that are clear, usable, and auditable.

That is where the 5Ts come in - five deliverable types that turn assembled agents into usable work: a consultation, a document, a table, a transaction, or a managed task.

Talk

The Consultant

A consultation grounded in the right data and context that helps a user work through a decision, question, or next step.

Template

The Scribe

A document pre-filled with the right data and context that helps a user produce a prior authorization, clinical summary, or a policy-based response.

Table

The Extractor

A structured result drawn from unstructured sources like PDFs and clinical notes that helps a user review, compare, and prioritize what needs attention.

Transaction

The Doer

An action completed with the right data and context that helps a user submit, update, or route work in a connected system, like scheduling appointments.

Task

The Manager

A follow-up step created from the right workflow context that helps a user assign, track, and move work forward.

The 5Ts define the kinds of work assembled agents can deliver. The next step is building more of them.

Agents Assemble Challenge

The platform is live. The ecosystem is open. Now it’s time to build.

The 5Ts define what useful healthcare AI delivers, and the challenge is where builders can put that into practice.

Through the Agents Assemble Challenge, we’re inviting builders to design agents that combine generative reasoning with defined outputs - and to test how deterministic workflows and adaptive intelligence can work together in real healthcare settings.

This is not a closed system.
It’s an open ecosystem.
And we’re building it together.

If you believe AI should deliver more than chat, build with us.

Design agents for real outcomes.
Publish them on open standards.
Help define what production-ready healthcare AI looks like.

Launched March 4. Submissions are open now.

Talk to Us

Building an agent or deploying one into a real workflow? Let’s talk.

Share your use case, and we’ll help you find the right starting point with Prompt Opinion.