.png)
.png)
An agent orchestration layer is the coordination and governance system that sits above individual AI agents, whether they run inside any EHR, Workday, Salesforce, or a point solution, so that work handed off between agents completes end-to-end instead of stalling at the boundary between two systems that were never designed to talk to each other. Most health systems building an AI strategy right now have agents. Very few have an orchestration layer, and the gap between the two is quickly becoming the most expensive unsolved problem in enterprise healthcare AI.
Why this has become the conversation every CIO is having
Talk to enough health system technology leaders right now and a consistent picture emerges. Every major enterprise vendor is shipping its own agents: Epic is building an agent factory, Workday has agents, Salesforce has agents, and a long list of point solutions layer agents on top of scheduling, documentation, and revenue cycle workflows. Each of those agents works reasonably well inside its own walls. The problem shows up at the boundary. An agent in one system hands a task to a process in another, and nothing coordinates what happens next, what gets escalated, what gets logged, or what happens when one agent's output doesn't match what the next step expects.
That's the exact failure mode health system leaders describe when asked what's missing: not a shortage of agents, but the absence of anything watching all of them at once. Left unaddressed, the result is a new version of an old problem. The industry spent a decade building point solutions that didn't talk to each other, and it is now at real risk of building point agents with the same flaw, just with a more sophisticated label on top.
It's not only about coordination
The health system leaders raising this issue consistently push the definition further than simple task handoff. An orchestration layer that only routes work between agents is solving half the problem. The other half is that AI agents don't behave like the automation that came before them. Traditional RPA did the same thing every time, predictably, and predictably meant it was easy to trust. An AI agent adapts to input, and that same flexibility means it can also drift: performing differently on a case it hasn't seen before, degrading quietly as underlying data shifts, or behaving in a way nobody explicitly programmed and nobody is watching for.
Add governance on top of that, specifically the requirement that PHI and PII moving between agents stay secured and auditable at every handoff, and the shape of the actual ask becomes clear: coordination, continuous oversight, and governed data movement, together, not as three separate purchases.
Where Gravity's orchestration layer fits
Gravity is built around the same three requirements, as architecture rather than as a feature added after the fact.
A unified context layer instead of per-agent silos. Every agent running on Gravity, across revenue cycle, population health, and patient access workflows, draws from the same governed layer of clinical, financial, operational, and payer data. An agent handling a referral and an agent handling the downstream eligibility check are working from the same patient context, not two disconnected views that happen to reference the same person.
Agent Studio, with guardrails built in rather than bolted on. Health systems extending beyond Gravity's prebuilt agents build inside Agent Studio, a no-code environment with healthcare knowledge bases and governance guardrails already loaded, so a newly built agent inherits the same context, permissions, and oversight rules as everything else running on the platform, instead of becoming its own island.
Configurable human-in-the-loop at every node, not a binary switch. Every workflow can be configured with human approval, review, or full handoff at any step. Routine, low-risk work runs autonomously. Anything touching clinical judgment or a sensitive account decision routes to a person, with the context of what the agent already found attached, so the person isn't starting from a blank page.
Data observability that watches before agents act, not after something breaks. Gravity monitors data arrival, schema changes, completeness, and AI-readiness continuously, so an agent's inputs get flagged as unreliable before that unreliability becomes an incorrect action a human has to catch downstream.
Stack-agnostic by design. Gravity runs on AWS and Azure, works with Snowflake and Databricks, and supports OpenAI, Anthropic, and Meta models, which matters directly for orchestration: a layer that only coordinates its own agents isn't solving the cross-vendor problem health systems actually have.
Models and model benchmarking
The other pattern showing up consistently in how health systems talk about AI strategy is that model choice is never one decision. It's dozens of smaller decisions, each shaped by how much risk the use case carries. A model drafting a discharge summary for a physician to review carries different requirements than a model triaging an inbound call or answering a benefits question. Health systems are increasingly explicit about this: frontier general-purpose models for lower-risk operational and administrative work, more rigorous, sometimes clinically validated or reviewed models for anything touching diagnosis or treatment, and a willingness to swap either one out as better options become available.
That last point is where benchmarking becomes an ongoing discipline rather than a one-time vendor selection. Health systems are running structured comparisons between platform vendors and point solutions before committing, and increasingly asking not just "does this model perform well today" but "what happens when a better or cheaper model exists in six months, and does switching to it require rebuilding the agent." Token costs are rising as usage scales, and new frontier models arrive often enough that a model choice locked into the agent's architecture becomes a liability, not a decision made once and forgotten.
Gravity's model-agnostic architecture is built for exactly that reality. Because agents run on Gravity's context layer rather than being hard-wired to a specific model, a health system can benchmark models against its own production workflows, swap in a better-performing or lower-cost model as one becomes available, and keep the governance and human-in-the-loop configuration intact through the change. The model is a component that gets evaluated and replaced. The context, the guardrails, and the audit trail don't have to be rebuilt every time it does.
What to actually ask an orchestration vendor
Most vendor conversations right now describe orchestration in nearly identical terms, and health system leaders are openly skeptical of the category as a result, because a slide that promises coordination, oversight, and governance is easy to make and hard to verify. The more useful question isn't whether a vendor's orchestration layer coordinates the agents it built. It's whether that layer can coordinate, monitor, and govern an agent built somewhere else entirely, on a different vendor's platform, using a different model. That's the actual test, and it's the one most point vendors can't pass, because their orchestration only extends as far as their own ecosystem.
Frequently asked questions
What is an agent orchestration layer in healthcare? An agent orchestration layer coordinates AI agents running across different systems, EHRs, ERPs, CRMs, and point solutions, so that a task handed from one agent to another completes end to end, with continuous monitoring of agent behavior and governed movement of PHI and PII between systems.
Why isn't agent coordination enough on its own? Because AI agents behave differently from traditional automation. They adapt to input, which means they can also drift or degrade in ways that are hard to detect without active monitoring. An orchestration layer that only routes tasks between agents, without watching for that drift, misses half of what health systems actually need.
Should a health system use one AI model for everything? No. Model choice should match the risk of the use case: frontier general-purpose models for lower-risk operational work, and more rigorously validated models for anything touching clinical decisions. Because models and their costs keep changing, health systems increasingly treat model selection as an ongoing benchmarking process rather than a single vendor decision.
What should a health system ask before choosing an orchestration platform? Whether the platform can coordinate and govern agents built on other vendors' systems, not just the agents it built itself. Orchestration that only spans one vendor's own ecosystem doesn't solve the cross-platform coordination problem most health systems actually have.
See how Gravity's orchestration layer coordinates agents, monitors for drift, and keeps humans in the loop, across your existing stack, not just Innovaccer's.
