AI Agents in Healthcare: When AI Stops Answering and Starts Acting

Why healthcare leaders need to understand the difference and why AI capability should never be confused with AI authority

Artificial intelligence is moving quickly from generating information to participating in the work itself. Until recently, much of the healthcare conversation focused on what generative AI could create: summaries, emails, policies, reports, educational materials, documentation, and answers to questions. The conversation is now shifting toward AI systems that can pursue a goal, navigate a workflow, use tools, evaluate information, make recommendations, and potentially take action. This is where AI agents enter the conversation, and for healthcare organizations, the distinction matters because the governance question changes as AI moves closer to decision-making and action.

When AI is primarily generating an answer, organizations naturally focus on whether the output is accurate, appropriate, and supported. When AI begins participating in a workflow, the more consequential question becomes what the system is actually permitted to do. Healthcare leaders therefore need to understand not only what an AI system produces, but what systems it can access, what decisions it can influence, what actions it can initiate, and where meaningful human authority remains. The governance question moves from “Is the answer accurate?” toward “What is this system allowed to do?” That is a fundamentally different governance problem.

What Are AI Agents in Healthcare?

At its simplest, I think of an AI agent as an AI-enabled system that can pursue a defined objective through multiple steps. Rather than requiring a human to prompt every individual action, an agent may be designed to understand a goal, determine what needs to happen next, retrieve information, use tools, evaluate results, continue through a workflow, and ultimately escalate or complete a task. The significance is not simply that the system is more sophisticated. It is that the AI can begin operating across a process rather than responding within a single interaction.

This distinction is useful when comparing generative AI with agentic AI in healthcare. Generative AI primarily produces; an AI agent pursues. A generative AI system may respond to a prompt with a summary or recommendation, while an AI agent can potentially take that recommendation into a broader workflow, interact with other systems, retrieve additional information, and initiate subsequent steps. From an AI governance perspective, that shift from production to participation matters because each additional capability can introduce another layer of authority and another potential consequence.

How Agentic AI in Healthcare Changes the Workflow

A generative AI model may receive a prompt and generate a response. An AI agent typically surrounds that intelligence with additional capabilities, which may include defined goals and instructions, access to trusted knowledge and data, memory or workflow state, tools and external systems, orchestration logic, permissions, monitoring, and governance controls. Together, those components allow the system to move through a process rather than simply respond within a conversation.

Consider a healthcare compliance example. A generative AI system could be asked to summarize new CMS guidance and provide that summary to a compliance professional. A regulatory-intelligence agent could operate very differently. It might monitor an approved CMS source, identify that new guidance has been published, retrieve the underlying document, compare it with existing requirements, identify potentially affected compliance areas, assign a preliminary risk level, assemble supporting evidence, and route the issue to the appropriate compliance professional for review. At that point, the system has become a participant in the workflow, and its governance cannot be limited to evaluating the quality of the text it generates.

The architecture therefore becomes part of the control environment. Once an AI system can access information, invoke tools, interact with systems, or initiate actions, leaders need to understand the permissions surrounding those capabilities. A system that can read information presents a different control question from one that can write information, and a system that can recommend an action presents a different risk from one that can execute the action without additional approval. As AI capability increases, healthcare organizations need to define those boundaries deliberately rather than allowing technical capability to determine operational authority.

AI Capability Is Not the Same as AI Authority

As AI becomes more capable, organizations may be tempted to focus primarily on everything the technology can do. I believe healthcare leaders need to ask a different question: What should we allow it to do? Capability and authority are related, but they are not interchangeable. An AI system may technically be capable of performing an action that the organization has no intention of delegating to it.

I often think about AI authority as a progression: Assist → Recommend → Decide → Act. Each step represents a meaningful increase in authority and, potentially, in risk. An AI system that helps a compliance professional locate a regulation has a very different risk profile from an AI agent that independently determines a claim was improperly billed and initiates corrective action. The underlying technology may eventually be capable of both activities, but that does not mean the organization should authorize both.

This is why one principle has become central to how I think about AI governance: capability is not authority. Before an AI agent receives meaningful autonomy, leadership should establish what the system is authorized to do, what it is prohibited from doing, what requires human approval, and what circumstances require the system to stop and escalate. Those decisions are governance decisions, not simply technical configuration decisions.

Human Oversight of AI Requires More Than a Human in the Loop

We frequently hear that organizations should keep a “human in the loop.” As AI becomes more agentic, I believe that concept requires much more definition. A human clicking “Approve” at the end of an AI-driven process does not necessarily constitute meaningful oversight. The more important questions are whether that person has the expertise to challenge the AI, access to the underlying evidence, sufficient time to evaluate the recommendation, authority to override it, clear escalation pathways, and accountability for the final decision.

That is why I increasingly think in terms of Human-in-Governance™, rather than simply human-in-the-loop. Human oversight should establish the boundaries within which AI operates and should remain meaningful throughout the workflow. Leaders need to determine what the AI can assist with, what it can recommend, what it can decide, and what it can act on independently. They also need to determine what evidence must accompany a recommendation, when human approval is mandatory, what thresholds trigger escalation, who owns the final decision, and who has the authority to restrict or stop the system.

The objective is not to insert a human into a workflow simply so an organization can say a human was involved. The objective is to preserve meaningful human authority and accountability where the consequences warrant it. The question is not merely whether there is a human in the loop, but which human, with what expertise and authority, is actually governing the AI.

Why AI Governance in Healthcare Raises the Stakes

This distinction matters in every industry, but healthcare introduces additional complexity because AI-supported workflows may touch clinical information, coding and billing, medical necessity, regulatory interpretation, payment decisions, fraud and abuse investigations, privacy, credentialing, utilization management, compliance audits, and patient-facing decisions. An error may therefore create consequences that extend well beyond an inefficient workflow. Depending on the application, the consequences can be regulatory, financial, professional, legal, operational, or directly related to patients and members.

That is why healthcare AI governance needs to mature alongside AI capability. If an organization moves from AI that assists to AI that recommends, decides, or acts, its controls should not remain static. Increased autonomy should bring increased attention to authorization, oversight, auditability, escalation, monitoring, and accountability. As autonomy increases, governance must increase with it. 

The issue is not whether healthcare organizations should use increasingly capable AI. The more useful question is whether the organization understands the consequences of giving that capability authority within a particular workflow. An agent operating in a lower-consequence administrative process may require different controls from one that can influence reimbursement, clinical operations, compliance determinations, or patient-facing decisions. AI governance should therefore be proportional to both capability and consequence.

Before You Deploy an AI Agent, Define the Governance Boundaries

Before giving an AI agent access to a healthcare workflow, leadership should be able to answer several fundamental questions. What specific problem is the agent solving, and what is its defined purpose? What information and systems can it access, and which sources are considered authoritative? What actions is it authorized to take, and which actions are explicitly prohibited? Which decisions require qualified human review or approval, and what thresholds should trigger escalation?

The organization should also be able to reconstruct what happened. Can the evidence used by the agent be identified? Can the actions taken by the system be traced? Can human approvals, overrides, exceptions, and escalations be documented? Who is accountable when something goes wrong, and who has the authority to modify, restrict, pause, or discontinue the agent? These are not questions to answer after implementation. They are part of determining whether the proposed deployment is sufficiently governed to proceed.

Monitoring also needs to extend beyond traditional performance measures. Organizations should establish how they will identify errors, unexpected behavior, excessive overrides, inappropriate actions, changes in performance, and changes to the underlying model, data, integrations, or vendor functionality. A capable agent that operates without effective monitoring can create problems at a speed and scale that a traditional manual workflow could not. The ability to intervene therefore needs to be designed into the system before intervention becomes necessary.

If an organization cannot answer these questions, it may have an impressive AI capability, but it does not yet have a fully governed AI capability. Governance is not simply a document describing how the technology should be used. It is the operational structure that establishes authority, controls behavior, monitors outcomes, and preserves accountability.

The Future of AI Governance in Healthcare

Healthcare organizations are entering an important transition. Governance models developed for employees using generative AI to draft emails, summarize documents, or assist with routine content may not be sufficient for AI systems that can initiate tasks, interact with other systems, make recommendations, and take actions. The governance model has to evolve because the nature of the technology’s participation in the workflow has changed.

We will need to govern not only AI outputs, but increasingly AI authority. That means understanding the architecture, mapping the workflow, establishing decision rights, defining permissions and prohibited actions, validating trusted information, creating escalation thresholds, maintaining audit trails, monitoring performance and behavior, and preserving meaningful human accountability. These controls should be designed into the deployment rather than bolted on after the technology has already become embedded in operations.

This is where healthcare compliance has an important advantage. The discipline already understands risk assessment, ownership, policies, internal controls, training, auditing and monitoring, investigations, root-cause analysis, corrective action, and validation. The challenge is applying that discipline to systems that can operate with greater speed, scale, connectivity, and autonomy. Agentic AI does not eliminate the need for a control environment; it makes the quality of that control environment more consequential.

The question for healthcare leaders is therefore no longer simply, “Are we ready to use AI?” It is becoming, “What authority are we prepared to give AI, and do our governance capabilities justify giving it that authority?” That question should be answered before an agent is permitted to move meaningfully through a healthcare workflow.

AI agents may fundamentally change how healthcare work gets done. They may reduce administrative burden, accelerate information gathering, support compliance professionals, and improve workflow efficiency. But greater capability should never be mistaken for a reason to surrender organizational authority. AI may participate in the decision. AI cannot own accountability for the decision. 

Perhaps the simplest way to remember the distinction is this: Generative AI produces. AI agents pursue. Governance determines how far they are allowed to go.