Guardrails like pre-approved scripts and hard handoffs keep AI agents in check, ensuring outputs stay predictable instead of wandering into hallucinations.
The term “AI agents” is exploding across the media since, for the first time, the software doesn’t need to wait for a prompt before it pursues its goal. These digital assistants can move data and execute decisions across systems based on built-in logic.
Names like Hippocratic AI, Flok Health’s Kirsty, WoundAIssist, and Theos Health are early proofs of a shift. They’re stepping into the gaps created by a projected 86,000-physician shortfall by 2036 and the 40% of doctor time still chewed up by paperwork. They are AI agents that follow up after discharge, triage issues, and escalate without leaning on already overextended staff.
Which begs the question: Would you allow an AI agent to organize your medical calendar and send you lab results instead?
The Plumbing That Matters
Healthcare’s Achilles’ heel is still interoperability. Seven in ten providers say poor data exchange slows care. AI agents wedge themselves in between EHRs and patient engagement tools, pulling the right data at the right time and initiating the right workflow.
Red tape often holds the healthcare industry back from introducing such developments. FHIR, for instance, remains part of the story, but it’s too rigid to be the whole answer. The Model Context Protocol (MCP) is the emerging backbone that lets agents coordinate and trigger actions across otherwise disconnected systems. It provides a standard way for AI systems, like large language models (LLMs), to interact with other tools.
We aim to improve healthcare digital infrastructure to the point where we can add a new model or app without ripping out the old stack. Hospitals must run 24/7. This plug-and-play infrastructure is designed to allow innovation without disrupting the entire operation.
By breaking tasks down and creating specialist agents, for instance, one that helps diagnose, and one that retrieves clinical records, each agent stays narrow and reliable; it only has to master one domain, which means fewer edge cases and ambiguities. The system as a whole delivers end-to-end intelligence. MCP is designed to route the right job to the right agent in real time.
In practice, if a patient asked a chatbot, “According to my last blood test, how is my cholesterol?” The chatbot (an AI client) would send an MCP request to an MCP server for the patient’s health records. The MCP server, after confirming the patient’s consent and authentication, would retrieve the specific cholesterol values and send them back to the chatbot.
Then, the chatbot processes the lab values and shares them with the patient in simple language. It can also schedule a follow-up appointment by interacting with an MCP-connected scheduling system.
For hospitals to seriously adopt MCPs, they need infrastructure readiness, such as API-first architectures and clean data flows. They also must have governance maturity.
See also: AI Agents are Reasoning with Tools: What MCP Means for Autonomy
Guardrails: Where Agents Break, Systems Fail
Here’s the current imbalance: over 80% of AI evaluations in healthcare obsess over technical metrics like task completion and accuracy. Fewer than half assess human, safety, or economic outcomes. AI agent providers must ensure all of the above.
HIPAA ensures the privacy and security of protected health information (PHI). Every action should be encrypted, logged, and bound by role. However, frameworks like HIPAA and HITRUST don’t prevent hallucinations or unsafe decision-making in agentic AI.
Governance also means forcing agents to work within clinically pre-approved scripts. Trust collapses the moment an agent steps outside its swim lane. AI agent providers and healthcare institutions, together, must define human-in-the-loop policies and escalation thresholds. These will confirm when AI acts and when it hands off to a clinician, eliminating AI’s autonomy in high-risk scenarios where hallucinations or incomplete logic could harm the patient.
Some of the obvious thresholds that don’t invite AI judgment calls include: Chest pain or shortness of breath, uncontrolled bleeding, sudden or severe swelling, or high fever (>38.5°C / 101.3°F) after surgery.
Edge cases, where the AI agent must confirm inputs before escalating, might include ambiguous language. If a patient says their leg feels weird, practitioners must clarify whether the agent prompts for clarification or escalates to a nurse.
MCP-driven interoperability combined with HIPAA-compliant data flows often offers sufficient safeguards for pilot AI deployments.
The Jobs Worth Automating
Agentic AI promises to automate tasks and has the potential to abolish administrative burdens if use cases are chosen wisely. Omega Healthcare, for instance, recently implemented AI-based automation to process over 100 million transactions, saving 15,000 employee hours per month. Its success relies on choosing repeatable, low-hanging fruit tasks where inputs and outputs are structured. Workflows that remove the room for hallucination and guesswork.
The safe, scalable examples include:
- Rescheduling triggered by no-shows
- Escalating when the patient’s language signals risk
- Reconciling downstream appointments when diagnoses are updated
- Routing lab results with built-in follow-up scheduling
- Checking adherence to care plans with tight escalation rules
In all scenarios, the AI doesn’t improvise; it scores pain against preset thresholds and routes the case to the right clinician. Guardrails like pre-approved scripts and hard handoffs keep it in check, ensuring outputs stay predictable instead of wandering into hallucinations.
The organizations that treat agentic AI as workflow infrastructure will win. The stack that matters is one where interoperability is fluid and agents are trained to do less and better. Careful governance built in from day one ensures the right tasks are tightly bound at scale.
