The secure engine every agent runs on.
Healthcare needs many agents working alongside many people — coordinating, remembering, and staying inside the rules. It's the HIPAA-native engine that runs them, connects them, governs them, and proves they're working.
One case · A team of agents
Picking the right model is important — but even more important is everything around it: coordinating a team of agents, keeping shared memory across handoffs, connecting to payers and EHRs, and proving every action was safe and auditable. That's what a platform is for — and it's what turns a clever demo into a system a healthcare team can run on.
Eligibility, prior auth, and denials agents have to coordinate on the same patient without stepping on each other. That's orchestration, not a single prompt.
Run layer · Multi-agent & A2AA multiplayer system handles real patient data across many actors. Tenant isolation, PHI redaction, and audit can't be add-ons — they have to be the floor.
Govern layer · Isolation & auditWith agents acting on PHI, "looks right" isn't enough. Continuous evaluation, hallucination metrics, and tracing are the difference between a demo and a durable system.
Improve layer · Evaluation & tracingThe Agent Platform is organized into five layers. Together they provide everything needed to build, run, connect, govern, and continuously improve agents at scale — in a regulated healthcare environment.
Healthcare-specific agent templates, a targeted Agent Catalog, and a Skills Library. Start from proven patterns — every deployment makes the next faster.
The secure runtime for operating agents at scale — orchestration, durable sessions, and memory that holds context, consistent across workflows.
Secure connections to the systems agents depend on — FHIR and HL7 clinical data, an MCP gateway for agent tools, OAuth2 connectors, and RAG over your knowledge base.
Governance built into the runtime: a 3-layer harness, RBAC/SSO/MFA, PHI redaction, a HIPAA perimeter, KMS encryption, tenant isolation, clinical boundaries, and a 7-year audit trail.
Agents that get better in production: continuous evaluation, full agent traces, an agent optimizer, token-budget controls, and hallucination monitoring.
Healthcare AI runs in a different environment than enterprise productivity. PHI moves through nearly every workflow. Payer integrations are non-negotiable. Audit trails are required. Clinical boundaries matter. Staff operate under time pressure with no tolerance for unreliable agents.
Generic AI platforms can be adapted for healthcare. The GraymatterLab Agent Platform was built for it — every layer reflects the data sensitivity, integration complexity, compliance requirements, and human accountability that regulated care demands.
Human-in-the-loop is structural here — a design principle, not a feature added later. Regulated care leaves no tolerance for unreliable agents.
Browse the Gallery — a growing set of standalone healthcare agents your team can pick and put to work on demand. Each is built from real deployment experience, then tested and governed — so you start from what already works, not a blank sheet.
Each one tested & governed — and the gallery grows with every deployment.
The Agent Platform doesn't operate in isolation. It's the engine underneath Cowork — the workspace where people and agents do the daily work — and the infrastructure every Delivery Playbook deploys agents into. That integration is what makes the GraymatterLab AI Operating System coherent.
The HIPAA-native engine that runs, connects, governs, and improves every agent — the foundation both build on.
Each one makes the others more effective.
We'll walk through how the five layers work together in a real workflow — prior authorization, infusion coordination, DME documentation, or the domain most relevant to your team.
One engine, built for healthcare — running, connecting, governing, and improving every agent.
