The Changing Landscape of AI in Medicine: Ordering Through AI Agents

Today’s pharmacy apps and telehealth platforms already use AI-driven features to simplify routine medication tasks. For example, CVS Health recently introduced a conversational AI chat in its mobile app that lets patients check prescription refills, track order status, and get reminders.

Walgreens likewise uses chatbots on its website to help customers with “prescription-related matters,” answering common questions and guiding users through basic refill processes. New digital pharmacies extend this trend: platforms like NowPatient offer virtual medication management, including AI-powered reminders and educational tools.

As one summary notes, NowPatient provides “AI-powered health tools” such as medication reminders and chronic disease tracking for users in the U.S. In short, we already see medication-ordering AI handling things like auto-refills, delivery tracking, and dose reminders, giving patients timely alerts and information without a live human.

Assisted Prescribing: AI + Clinician in the Loop

The next wave of innovation doesn’t replace the doctor but augments them. Companies like Curai Health and K Health use AI-assisted intake to accelerate virtual visits, with clinicians making the final decision.

• In Curai’s chat-first system, a patient describes symptoms and history in a conversational interface, then is connected to a licensed clinician who reviews the AI-collected data and writes any needed prescriptions.
  • In practice, Curai advertises on-demand primary care with near-instant access to a doctor, leveraging AI in healthcare “to superpower patient care teams” and deliver care “beyond what’s humanly possible”.
• K Health follows a similar model: patients complete an online “Medical Chat” that automatically collects their intake and synthesizes it into a complete patient chart before the provider even logs in. The clinician then uses an AI co-pilot to review that chart,  getting quick summaries of diagnoses, care gaps, and recommended treatment options,  which saves time on paperwork.
  • As K Health’s site puts it, they “pair clinicians with advanced AI to provide data-driven, personalized care around the clock”. By handling the history-taking and chart preparation, these AI agents let doctors focus on clinical judgement. The result is faster access to care and reduced administrative burden on clinicians, without fully automating the actual prescribing decision.

The Next Step: Delegation

Looking ahead, we envision truly delegated AI agents acting directly on behalf of patients for ongoing prescription assistance. Imagine a patient’s trusted digital health assistant autonomously renewing chronic-care prescriptions (like blood pressure meds) or scheduling preventive treatments (flu shots, screenings) based on pre-set permissions. This would let AI handle routine medication follow-ups without a live doctor involved each time.

Of course, this “agent-as-proxy” model demands rigorous controls. Every AI agent must have a verifiable identity and only perform allowed actions. In real life today, even human delegates struggle: caregivers often share passwords or “impersonate” a patient in portals just to manage refills, creating confusing audit trails.

The rise of autonomous agents only magnifies that challenge. Without a proper delegation framework, systems “aren’t designed to distinguish ‘the customer’ from ‘the customer’s agent’,” and there’s “no clear audit trail showing who (or what) took which action”.

Going forward, any AI acting for a patient must carry a digital credential proving its authority, and its every API call must be logged. In practice, this means we need strong agent identity plus fine-grained permissions: for example, an agent might have the power to renew certain medications but not to prescribe new ones, and systems must enforce those scopes.

Detailed audit logs will track each refill request and decision, linking them back to the patient’s consent. As security experts note, tagging every action with the correct “identity context” is essential so that AI behavior can be audited against what the user authorized.

In short, delegated agents can be very useful, but only if we know who the agent is and what it’s allowed to do, with a complete record of its actions.

The Barriers That Still Exist

There are still major hurdles before fully autonomous medication agents become reality. Regulatory restrictions are the first: U.S. law today generally assumes a human “practitioner licensed by law” must authorize prescriptions. (A recent Congressional bill tried to change that: the 2025 Healthy Technology Act would have amended the Food, Drug, and Cosmetic Act to let AI or machine learning systems count as eligible prescribers – but it has not been enacted.)

In practice, this means any AI-driven refill or new prescription must still be issued under a physician’s license. Safety concerns are another big issue. Medication errors already affect patients at alarming rates – roughly one in 30 patients experiences some medication-related harm, and community pharmacies see about 1.5% of dispensed prescriptions with errors.

Introducing AI agents raises questions: will they reduce errors or accidentally create new ones. Regulators and providers will demand strong evidence that any automated system actually improves patient safety. Finally, systems integration is a challenge. U.S. healthcare data is notoriously siloed.

For example, community pharmacists typically cannot see a patient’s full health record, “pharmacists out in the community are isolated from the rest of the healthcare system,” because “nothing connects them with information about a patient”. Pharmacies lack software to seamlessly integrate hospital EHR data with their dispensing systems. Without better interoperability, an AI agent that needs to verify lab results or recent diagnoses before renewing a drug may simply hit dead ends.

In summary, legal limits on non-human prescribers, the high cost of medication mistakes, and the fragmented IT infrastructure all slow down more ambitious agent delegation today.

Identity Will Be the Enabler

Solving the delegation problem ultimately comes down to identity. The emerging MCP-I (Model Context Protocol – Identity) framework is designed to give AI agents verifiable identities and delegated authorities in precisely these scenarios.

MCP-I extends Anthropic’s original MCP (Model Context Protocol) by adding an “identity-centric” layer. In this model, every AI agent carries cryptographic identity tokens and role-based permissions. For example, Vouched explains that “MCP-I introduces secure, identity-centric communication between human users and software agents,” so that “all participating agents can be authenticated and trusted in dynamic, automated environments”.

In practice, a human user first authenticates via an OAuth token, a passkey, or even a verified government ID, and then securely delegates that authorization to the AI medication agent. Vouched’s own MCP-I Server product shows how this works: it offers a turnkey solution that quickly adds “Know Your Agent” capabilities to any AI workflow.

• The service supports a wide range of identity types (from simple username/password or OAuth sessions to durable digital IDs and DIDs), and it securely stores each agent’s granted authorizations and roles.
• Vouched also highlights that MCP-I includes built-in agent reputation tracking and consent frameworks – effectively recording who granted what powers to which agent. In this way, strong identity protocols transform a generic “AI” into a named, permissioned agent bound to a real person.
• This means a patient’s agent can present a signed credential saying, “I am Alice’s pharmacy bot, authorized to renew her diabetes medication,” and the pharmacy system can verify it.

Armed with MCP-I identity and audit trails, systems can safely trust delegated refill requests while retaining full accountability. (For more on Vouched’s approach, see its MCP-I Server solution and Know Your Agent offerings, which implement these concepts in production.)

What’s Next?

Over the next 2–5 years, we expect agent-led medication management to grow rapidly, especially in chronic and remote care.

• AI agents will move from reactive to predictive care: for example, a diabetes-management agent might continuously monitor glucose readings (via wearables) and proactively order a prescription refill or flag a dosage change before the patient even realizes an issue.
• We’ll see smarter personalized medicine: agents analyzing each patient’s history to tailor preventive treatments, say, adjusting statin orders based on genetic and lifestyle factors, and ensuring adherence.
• In chronic disease management, systems like smart pill dispensers and medication reminder apps (already helping seniors stay on schedule) will be integrated into the agentic workflow.
• A heart-failure patient’s agent might schedule lab tests, coordinate with caregivers, and arrange refills with the cardiologist’s approval, all automatically.
• Long-term care facilities could employ delegated agents to manage complex drug regimens across nurses and doctors, while keeping detailed logs of every change.
• In remote or rural telehealth, AI navigators will handle triage and basic orders around-the-clock, handing off to human clinicians only when needed.
• Industry experts predict these trends: Automation Anywhere notes that AI agents will increasingly enable predictive and preventive care, helping identify health risks early and recommending interventions to improve long-term outcomes.
• Similarly, voice assistants and monitoring bots in patients’ homes will “optimize care and reduce the chance of errors” by continuously collecting data and prompting timely action.

In short, expect agentic systems to become a standard part of managing chronic conditions, remote monitoring, and routine preventive care.

Final Thoughts

Delegating medication tasks to AI can transform healthcare – but only if it’s done with trust built in. Agent delegation and strong identity are the linchpins of this future.

By embedding verifiable digital identities and auditable permission sets (through MCP-I or similar protocols), every agent action can be traced back to a real patient’s approval. This gives regulators and providers confidence that “smart” refills are in fact patient-authorized refills.

As one expert notes, the combination of detailed audit trails and identity-aware monitoring will be critical to “maintaining control, visibility, and trust” as AI agents become more embedded in care. With these safeguards, the promise of virtual medication management and prescription assistance can be realized: efficient, patient-centric medication care that speeds up access without cutting corners on safety or privacy.

Vouched’s work in this space – from MCP-I solutions to “Know Your Agent” identity services – exemplifies how the industry is laying the groundwork today for a compliant, AI-powered pharmacy of tomorrow.

In that agentic future, patients will enjoy seamless, automated support with their medications, while clinicians and regulators retain the accountability and oversight needed to keep care safe and effective.

See how MCP-I and agent delegation can bring trust, compliance, and automation to your healthcare platform. Interested in Know Your Agent (KYA)? Visit our page to learn more