Agentic AI in Healthcare: Can Clinical Governance Keep Pace?
- Khwahish Sharma
- 15 hours ago
- 5 min read
Medora Advisor’s Summary
Agentic AI in healthcare is moving faster than the governance built to hold it. These systems don’t just suggest anymore. They act. Most of the rules we lean on were written for software that waits for a human to click “approve.” The space between what these agents can do and what we can actually oversee is widening. Here’s what that means for your organization, and why governance has to come first.
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Let’s break this down together. Something quiet but important is happening in health systems right now. AI has stopped waiting for permission. The tools we used to call decision support are starting to make moves on their own. And the governance designed to keep them safe was built for a slower world.
Are we deploying agentic AI faster than we can govern it?
It feels like every week brings another AI announcement. But one moment stood out. On 1 December 2025, the US Food and Drug Administration deployed agentic AI for all of its employees — systems it describes as achieving goals by planning, reasoning, and executing multi-step actions [1].
Read that again. The agency that reviews medical devices is now running agents internally.
That tells you where this is heading. Agentic AI is being adopted faster than clinical governance can keep up. The two are not moving at the same speed.
Traditional models offered a recommendation and waited. Agentic systems have the autonomy to act. They chain tools together, pull data, trigger orders, and adjust steps without a prompt at each turn. In a hospital, that could mean flagging a deterioration, ordering the labs, and drafting a protocol before the attending arrives.
Honestly? That’s exciting. It’s also exactly where things get tricky.
What does agentic AI actually mean at the bedside?
Picture the same moment, two ways.
In the first, a tool flags a possible sepsis case and waits for a clinician to decide. In the second, an agentic system flags the risk, orders the labs, and drafts the first protocol on its own. That shift, from suggesting to doing, is the whole story.
Here’s the part worth slowing down on. A strong demo is not the same as safe behavior in a live ward. We have seen this gap before. The Epic Sepsis Model, used across hundreds of US hospitals, missed roughly two-thirds of real sepsis cases in one external validation, while firing alerts on nearly one in five hospitalized patients [2].
It looked good on paper. At the bedside, it created alert fatigue and still missed the patients it was built to catch.
There’s a quieter risk too. Bias rarely announces itself. A widely used care-management algorithm studied in Science gave Black and White patients the same risk score when the Black patients were, in fact, considerably sicker [3]. Correcting it would have more than doubled the share of Black patients flagged for extra care. A flawed proxy, acting at machine speed, turns a quiet bias into a fast one.
Now imagine handing a system like that the authority to act without asking. Only you can really tell whether your teams are ready for tools that move faster than your committees meet. That part isn’t a technical question.
Where exactly is the agentic AI governance gap?
The conversation keeps circling three gaps. Let’s name them plainly.
Accountability. When an autonomous agent makes a clinical decision that harms someone, who owns it? The developer, the health system, or the clinician meant to be supervising? Our governance models assume a human pulled the trigger. Agentic AI blurs that line.
Auditability. If an agent reroutes a pathway or adjusts a plan, auditors need to know not just what happened, but why. These systems often lack a clear, human-readable trail. That turns a clinical audit into a guessing game.
Human-in-the-loop. We love to say the clinician stays the final decision-maker. But if an agent fires dozens of actions a minute, real oversight gets hard without burning people out.
The fix isn’t a single brake. It’s risk-stratified guardrails. High-stakes calls, like dosing or ventilator changes, should need explicit human sign-off. Lower-risk admin tasks can run with more autonomy under tight audit. That balance sits at the heart of any serious clinical governance for agentic AI.
What are ADHA and the TGA signaling right now?
Regulators are starting to move, and Australia is a useful place to watch.
In March 2026, the Australian Digital Health Agency established a National Clinical Governance Committee for Digital Health, with a dedicated expert group on Artificial Intelligence Enabled Care [4]. The signal is clear: clinical safety and quality should guide how AI gets used, not trail behind it.
The Therapeutic Goods Administration is moving too. Its review of AI and software-based medical devices found that most stakeholders — 81 percent — want the rules redefined to clarify who is responsible for AI outcomes [5]. The worry is plain. Current law doesn’t clearly say who answers for the actions of adaptive, autonomous systems.
For System Integrators, this is the part to underline. Your provider and government clients are being told that safety and accountability come first. A bid that builds in clinical governance from the start is far stickier than one that treats it as a compliance afterthought. In this market, governance credibility is a competitive advantage, not a cost line.
Why governance has to be a day-one design choice
Here’s the honest part, and it depends on your culture. Bolting governance onto an agentic system after go-live rarely ends well.
At Medora Advisors, we hold a simple belief: clinical governance is a day-one design constraint, not a go-live checkbox. You design the guardrails alongside the system, not after it ships.
In practice, that means a few concrete things. Define which decisions need a human and which can run on their own, by risk tier. Wrap your AI estate with auditable trails and clear exception routing. Keep vendor choices neutral, because neutrality here is risk management, not indecision.
This is the work of Clinical Governance Framework Development — building the structure that lets you adopt agentic AI without holding your breath. AI can move data faster than any of us. It still can’t feel the weight of a hard call at 3am or read a worried family in a waiting room.
That’s the line a governance-first approach protects. Not just compliance. The trust that healthcare runs on.
A Final Thought
Agentic AI isn’t another software update. It’s a shift in who, or what, takes action inside your health system. The technology is racing ahead. Our job is to make sure the guardrails are already there when it arrives. Get the governance right, and these tools can genuinely lighten the load without putting patients or trust at risk. That’s the whole point.
Conversation Starters
If an autonomous AI made a wrong call in our hospital tomorrow, could we actually trace why it happened?
Should we be defining which AI decisions need a human sign-off before we roll anything else out?
Could clinical governance be the thing that makes our SI bids stickier, rather than the thing that slows them down?
Curious where your own governance gaps might be hiding? Let's talk it through.
References
[1] U.S. Food and Drug Administration. “FDA Expands Artificial Intelligence Capabilities with Agentic AI Deployment.” 1 December 2025. fda.gov
[2] Wong A, et al. “External Validation of a Widely Implemented Proprietary Sepsis Prediction Model in Hospitalized Patients.” JAMA Internal Medicine, 2021. jamanetwork.com
[3] Obermeyer Z, et al. “Dissecting racial bias in an algorithm used to manage the health of populations.” Science, 2019. science.org
[4] Australian Digital Health Agency. “New National Clinical Governance Committee for Digital Health drives collaboration and clinical safety.” 17 March 2026. digitalhealth.gov.au
[5] Therapeutic Goods Administration. “Clarifying and strengthening the regulation of Artificial Intelligence (AI)” — consultation outcomes, 2025. tga.gov.au
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