When people think of AI agents, they often picture a powerful Large Language Model (LLM) that can handle tasks with just a simple “prompt.” But building effective AI agents for healthcare is a whole different ballgame. 

These agents manage critical, multi-step workflows where the margin for error is virtually nonexistent. With incredibly high stakes, safety, accuracy, and stringent compliance are non-negotiable.

Consequently, developing production-ready, reliable and HIPAA-compliant AI agents for the healthcare industry not only demands advanced prompt engineering but a full ecosystem of solid backend tools, smart data pipelines, advanced analytics, and strict compliance frameworks. In this context, the prompt is really the foundation of a much bigger, highly connected system built to work seamlessly together.

Discover what truly differentiates enterprise-ready healthcare AI agents from consumer-grade solutions and why it matters.

What Specialized Prompt Engineering Is & Why It Matters 

Language models are essentially rich repositories of information. Our goal with prompting them is to provide clear, precise instructions and guidance, ensuring they produce responses that align with our desired outcomes. It involves a full process of writing, refining and optimizing outputs. 

Given the complexity of healthcare-related workflows, AI agents require explicit, highly structured instructions to successfully conduct natural conversations, all while adhering to strict safety and compliance guardrails. This is particularly critical in an MCP (Model Context Protocol) context where we craft prompts to support and leverage these complex instructions.

This meticulous approach enables agents to effectively handle ambiguous scenarios and complete entire workflows without skipping steps or fabricating information (aka hallucinations). Such considerations are fundamental to how we optimize agent prompts when developing our solutions.

Workflows like new patient appointment scheduling might seem like a simple conversation, but can actually be quite complex, involving numerous steps that can take more time than expected, like verifying a patient’s name, confirming insurance, reviewing appointment schedules, etc. If the agent fails at any stage of the conversation, the process falters, highlighting the significance of the explicitly detailed prompt itself. 

Effective Prompt Engineering Techniques 

Designing agentic AI for healthcare providers that’s safe and compliant involves a disciplined, multi-layered approach that integrates both technical expertise and strategic design. Below are some core techniques essential to the prompt engineering process:

1. Narrow Scope and Consistency to Create Reliable Healthcare Agents

For an agent to perform reliably in healthcare, it needs a clear job. For example, a scheduling agent should only focus on things like scheduling, rescheduling, or canceling appointments.  This can include tasks such as verifying patient identity, checking provider availability, navigating location preferences, selecting appointment types, sending confirmations, managing waitlists, handling appointment reminders, and following up on missed or canceled visits. 

When designers lay out exactly what an agent can and can’t do, it keeps the conversation on track. An overly broad prompt often yields unhelpful results from the agent.

2. Safety Guardrails to Prevent Hallucinations 

Prompts must include explicit “do/don’t” instructions to enforce safety. For example, an agent might be told, “You are not a doctor; do not provide medical advice.” These constraints prevent agents from making clinical judgments, offering diagnoses, or answering questions that should be handled by licensed professionals. Additional guardrails may include restrictions around accessing or referencing sensitive data, such as insurance information, prescription history, or protected health details unless verified through appropriate tools.

Agentic prompts within the healthcare space are also designed to ensure agents handle ambiguous responses appropriately. If a patient answers a yes-or-no question with “maybe,” the agent knows to re-ask the question until it receives a valid answer, rather than making assumptions. In high-stakes workflows, such as confirming surgical prep or managing medication instructions, these safety protocols ensure the agent stays within approved parameters, escalating to human staff when needed. 

3. Modular and Scalable Design

Writing a new, complex prompt for every customer or use case is inefficient. Instead, adopting a modular template system streamlines the process. A foundational “healthcare agent” template can include universal safety guardrails and ethical protocols, while a secondary “use case” template customizes the agent for specific workflows, such as scheduling or prescription refills. This approach ensures consistency while allowing for easy specialization.

Resist the urge to over-engineer agent prompts for a quick fix, as some vendors may throw everything into an agent prompt in service of quick implementation. While this might seem efficient for a fast go-live, it’s brittle and introduces risk. Whereas thoughtfully designed, intent-based MCP (Model Context Protocol) tools can increase performance, reduce the risk of hallucination and improve scalability.

4. Iterative and Flexible Prompts

Prompts must be designed for continuous refinement. A rigid or overly detailed prompt can lead to conflicts or unpredictable behavior. Modular, flexible prompts allow teams to quickly test and modify specific sections as needed without a complete rewrite. This iterative approach enables rapid improvements based on real-world feedback.

Measuring, Testing, and Improving AI Workflows

Testing and evaluation are critical to building reliable prompts. The process often begins by breaking down workflows from top to bottom into individual components and testing them in isolation. In simple terms: we have a goal of what the agent should be able to do from point A to point B, and so, how do we get it to point B?

Once these components are refined, end-to-end tests ensure they work together seamlessly. 

For example, for a scheduling agent, you would break down the process into multiple pieces, or checkpoints: verifying patient information, identifying why the patient is calling in, recognizing which providers the patient can see, confirming eligibility, etc. In order to create a unified experience, we would need to make sure each step works in an isolated fashion before stitching them together to successfully book the appointment. 

Think of It Like a Conversion Funnel

Once your AI agent is live, you need to keep a close eye on how it’s doing. This is where performance monitoring comes in. Think of observability dashboards as your mission control: they help you track important metrics, like how often the agent successfully completes a task, and pinpoint exactly where things might be going wrong.

You can essentially think of this example as a classic conversion funnel, where the patient comes in, and you have to go through all the checkpoints to complete scheduling. We’re always evaluating from this funnel perspective: is the agent doing what it wants? If not, where’s the drop-off, and how do we improve that? 

For example, our team noticed that name recognition was lower than expected (many patients were falling off), so we improved the way our agent was able to recognize names through some backend engineering that interacts with the prompt. With the change, our success rate for name matching increased by 46.15% for patients already in the system.

Another necessary component to evaluating and maintaining the lifecycle of agents at scale is a technique called “LLM-as-a-Judge” or LAJ. LAJ systems sift through transcripts and call recordings and score conversations based on things like task completion, compliance & safety, workflow adherence, agent errors, and patient experience. This feedback is gold for making the agent even better and reducing the burden of human evaluation on a timely basis. 

How Tools Enhance the Effectiveness of AI Agents

When we initially started building agents, we relied heavily on prompts to guide them. But we quickly learned that, instead, giving them the right tools is what really levels up the agent’s capabilities, helping it explicitly understand when and how to perform actions within the given context. 

Tools enable agents to understand what actions to take in a given context without overloading prompts with excessive instructions. By abstracting actions into the form of tools, the process becomes less error-prone, as the agent can choose from a predefined set of tools based on the situation.

For instance, when looking up a patient, a tool is used to facilitate the process of retrieving the necessary information. These tools perform backend API calls and return only structured, relevant data to the agent. The system is designed to limit an AI agent’s knowledge to only what’s necessary, reducing errors and hallucinations. Limiting responses minimizes misinterpretations and errors, thereby ensuring users fully complete the intended experience.

Having the right tools in place means we don’t need as many explicit instructions directly in the prompt (aka we don’t need to over-stuff them). We’re moving towards tools like MCP handling more of the information, acting as communication nodes for the agent to complete workflows. This shift will continue as language models get better and faster, allowing us to integrate improved solutions.

Prompts & Tools Must Work Together

As AI keeps evolving, prompts are getting shorter as models get smarter and tools become more powerful. We’re already seeing improvements in how AI “thinks through” complex responses. Multi-agent systems are also on the rise, with specialized agents handling tasks like patient verification or scheduling appointments. This modular setup makes them faster, safer, and easier to build.

In the future, better security and compliance will let agents take on bigger jobs, like processing payments or other high-trust tasks. But the key to success stays the same: combining a specialized, compliant prompt foundation with a solid system of tools, metrics, and constant improvement. Prompt engineering is important, but it’s just one piece of the puzzle for building safe, reliable AI agents for healthcare.

Today’s healthcare market is saturated with AI agent solutions, making vendor evaluation difficult for healthcare providers amidst similar claims and significant costs.

To simplify your evaluation, we’ve identified the top five factors that distinguish Artera’s AI agents today. Whether you’re new to AI agents or well into your research for a partner, we hope this distillation proves valuable.

Artera’s blog posts and press releases are for informational purposes only and are not legal advice. Artera assumes no responsibility for the accuracy, completeness, or timeliness of blogs and non-legally required press releases. Claims for damages arising from decisions based on this release are expressly disclaimed, to the extent permitted by law.

AI in Healthcare – FAQs

How can I evaluate the best AI agent platform for healthcare?

When considering what the best AI agent evaluation platform for healthcare is, look for solutions built specifically for the clinical, operational, and regulatory complexities of healthcare. Avoid platforms that are retrofitted from general-purpose AI tools. Key criteria should include HIPAA compliance, validated EHR integration, real-time performance monitoring, and governance frameworks that ensure safety, accuracy, and transparency. Artera delivers a purpose-built platform with a modular agent design trusted by more than 1,000 healthcare organizations and federal agencies.

What makes AI agents in healthcare different from generic AI assistants?
Healthcare AI agents must operate under strict regulatory frameworks (like HIPAA), manage complex multi-step workflows, and interact with sensitive patient data. Unlike general-purpose chatbots, they require structured prompts, safety guardrails, integration with clinical systems, and ongoing monitoring to ensure safety, accuracy, and trust.

How does Artera ensure its AI agents are safe and compliant?
Artera agents are designed with a healthcare-first approach. We do not use PHI or PII in model training, and our agents operate within a secure architecture that meets SOC 2 Type 2, HITRUST, and HIPAA compliance. Every agent follows strict governance protocols, real-time monitoring, and human oversight where needed.

What is a Model Context Protocol (MCP), and why is it important?
A Model Context Protocol is a structured way to deliver instructions, context, and tools to an AI agent. Instead of relying solely on prompts, Artera uses MCP to modularize agent behavior, improving accuracy, scalability, and safety across healthcare workflows.

Why does prompt engineering alone fall short in healthcare AI?
Prompts can guide the behavior of an AI agent, but without guardrails, backend tools, and integration into clinical systems, they can produce unpredictable or unsafe responses. In healthcare, where the margin for error is near zero, tools and testing infrastructure are just as critical as the prompt itself.

How does Artera’s modular approach support scalability?
Artera uses a layered design: a universal base agent for healthcare safety and compliance, and customizable “use case” templates for workflows like scheduling, intake, and referrals. This approach allows organizations to scale quickly while maintaining control and consistency.

Can Artera AI agents integrate with our existing EHR?
Yes. Artera integrates with all leading EHRs and digital health tools using secure API, HL7, and FHIR standards. This enables real-time data exchange and smooth workflow execution across your digital ecosystem.

How does Artera prevent AI agents from hallucinating or going off-script?
We combine prompt engineering with tool-based constraints, backend validations, and real-time performance monitoring. Techniques like “LLM-as-a-judge” help us assess agent behavior at scale, ensuring adherence to clinical and operational standards.

How can I start evaluating AI agents for our organization?
Start by identifying high-volume, low-risk workflows that are currently manual, such as appointment scheduling or reminders. Artera’s team can help you assess AI readiness, map your workflows, and develop a safe rollout plan tailored to your compliance, staffing, and tech stack.

The post Beyond the Prompt: Designing Agentic AI for Healthcare Providers That’s Safe, Scalable, and Compliant appeared first on Artera.

While tools like ambient AI scribes have made AI a common term in healthcare, the next frontier is automating high-volume operational tasks. To get there, you have three options: build your own solutions, buy off-the-shelf products, or partner with a specialized vendor.

Understanding Agentic AI

Agentic AI represents a sophisticated convergence of multiple technologies working together to create human-like interactions that can complete complex tasks autonomously. Unlike simple chatbots or single-function AI tools, these systems integrate multiple technologies, including large language models, AI-generated voice capabilities, speech transcription and new standards like MCP for connecting AI systems to software.

Here are some real-world healthcare applications of agentic AI:

• Appointment scheduling 
• Prescription management
• Password resets 
• Insurance collection
• Bill processing

Artera data reveals that a substantial 45 percent of call center inquiries revolve around appointment verification, cancellations, and rescheduling. These are prime candidates for AI automation.

These AI agents work around the clock, 24/7, delivering consistent service and taking care of routine tasks that used to require human staff. The technology has matured to the point where it can deliver human-like experiences that often outperform traditional call center performance in speed, accuracy and experience.

Build vs. Buy vs. Partner 

AI Tech Stack: “The Metal” 

Before we dive into the three approaches, let’s take a moment to break down the key layers needed to build an AI Agent. Think of these layers as building blocks, often provided by different vendors and serving various needs. I like to refer to this AI tech stack as “the metal.” 

• Large Language Models (LLMs): Understands & generates human-like text; there are many model providers, and the best depends on the use-case and modality (e.g. Gemini Flash 2.5 or OpenAI Realtime are the lowest latency as of this writing)
  • Vendors include Anthropic (Claude), OpenAI (ChatGPT), Google (Gemini), Meta (Llama), X1 (Grok), Deepseek (R1), Kimi (K2)
• Orchestration: Foundation layer that simplifies the process of integrating large language models with tools, while providing base observability and scalability for your agents
  • Vendors include AWS (Bedrock, Strands), Google (Vertex AI), Pipecat, LangChain
• Real-Time Voice: Includes two core services, often provided by distinct vendors depending on the use case. Speech-to-text (STT) transcribes audio into text, text-to-speech (TTS) turns LLM text output into spoken word, most typically using an “AI-generated voice” that is lifelike and infinitely customizable.
  • STT vendors include Google, Deepgram, Nuance and many others
  • TTS vendors include ElevenLabs, Cartesia, Murf, and many others
• Telephony: The core gateway connection into all the telecommunications and email (SMTP, POP3, IMAP) providers. For the most versatile agent, you will want to consider voice (PBX or SIP/VOIP), SMS, RCS, and email.
  • Vendors include Twilio, Sinch, Syniverse, Cisco and many more
• Tools: In healthcare, the “skills” you want your AI agent to fulfill (e.g. scheduling, prescription refill, insurance update) typically exist in the EHR/PM and are accessed via FHIR, HL7, SFTP or custom web services. As of this writing, best practice for leveraging tools involves creating a “translation” layer using an open standard like Model Context Protocol (MCP), which enables large language models to interact reliably with outside software. Think of MCP as the translator that allows AI agents to speak fluently with your existing healthcare systems, ensuring they can access patient information, update records, and complete transactions with the same reliability as human staff, but with greater consistency and availability.
• Evaluation: A high-performing AI agent requires significant prompt engineering, wherein the instructions for the LLM are tweaked to reliably achieve the desired outcome.  Evaluation frameworks allow your agents to simulate “real-world” situations to test for accuracy, safety, helpfulness, etc., before being deployed to production. The best AI systems will further implement a back-end “judge” that feeds real-life transcripts into the evaluation framework for continuous monitoring and improvement.

Building In-House AI Agents

The first approach, building an AI Agent in-house, demands substantial technical expertise and financial investment. This involves engaging with the lowest-level infrastructure and meticulously integrating the various components listed above. By carefully stitching these together, one could construct a bespoke solution. 

In this case, however, constructing a bespoke solution means you must work and maintain direct relationships with core AI Infrastructure providers to find solutions tailored to your needs, navigating a complex landscape and purchasing from each of them individually.

Advantages:

• Complete control over tech stack and implementation
• Customization aligned with specific organizational needs
• Highest quality product (provided the right supporting team)
• Lowest transaction costs (provided large enough volume)

Disadvantages:

• $3M minimum ongoing annualized investment (minimum of 2 Devops Engineers, 2 Machine Learning Engineers, 2 Senior Software Engineers, 1 Product Manager + hosting and software spend)
• Need for specialized in-house talent across multiple languages, e.g. Python for ML, YAML, JSON and JavaScript for SWE, and being current on various AI-specific standards like A2A and MCP
• Ongoing maintenance, all while staying up to speed with the latest technology advancements and agility to replace as new benchmarks emerge 

Purchasing an Off-the-Shelf AI Agent (OEM Vendor) 

Alternatively, health systems can work with a vendor that essentially rebrands a horizontal provider’s technology as their own: a practice known as OEMing (Original Equipment Manufacturing). OEM vendors provide a wrapper around existing middleware platforms (e.g. Vapi.ai or Bland.ai) that facilitate the creation of AI Agents, but are not specifically designed for healthcare.

This approach can mean paying double the actual technology cost while limiting direct access to the latest innovations and updates of the core provider within the AI Tech Stack or “the metal.”

Advantages:

• Single point of contact/vendor to manage
• Faster implementation timeline compared to building in-house
• Lower upfront costs compared to building in-house

Disadvantages:

• Higher long-term costs due to markup on underlying technology
• Limited customization capabilities
• Dependence on third-party middleware providers
• Less secure (additional entry point into your systems) 
• Less agility and control 

Another more preferable option (assuming in-house expertise around AI integration standards and baseline development capacity) would be a healthcare organization acting as its own OEM and integrating directly with the middleware platforms mentioned above. This will reduce vendor costs, while increasing maintenance spend and providing more direct control over the end product.

Partnering with AI Healthcare-Specialized Vendors

To steer clear of the above issues, I suggest health systems skip third-party vendors and work directly with those plugged directly into the metal. 

Which leads us to the third option: contracting with a vendor focused exclusively on healthcare (healthcare-vertical providers), who have direct access to those in the AI tech stack. Specialized healthcare AI vendors typically invest millions or more in developing healthcare-specific solutions, understanding the unique requirements of healthcare interoperability, security, and compliance, while maintaining direct relationships with core AI infrastructure providers.

Essentially, these vendors orchestrate and bundle all of the AI tech stack components / technology into a simple solution for healthcare providers, making it the fastest and most cost-effective way to deploy AI Agents. 

Advantages:

• Healthcare-specific expertise and compliance knowledge
• Direct integration with core AI infrastructure
• Proven real-world deployment experience
• Shared investment in healthcare-relevant innovations
• Faster time to value with lower risk
• Many customization capabilities 

Disadvantages:

• Dependence on partner’s technology roadmap
• Limited control over underlying technology decisions

The Core Tradeoff: Control Versus Speed

At its core, the build versus buy versus partner decision comes down to a tradeoff between control and speed.

Building in-house gives you full ownership of your tech stack, deeper integration into internal systems, and full control of sensitive data. However, it often requires longer development timelines, and significant upfront and ongoing investment

Buying off-the-shelf may offer faster deployment and lower initial costs (versus building), but can limit your ability to customize, restrict access to core data, and create long-term scalability issues.

The more strategic approach for many healthcare organizations is to partner with a vendor that understands the unique complexity of healthcare systems. By working with Artera, health systems accelerate time to value, leveraging years of specialized expertise, a compliant AI infrastructure, and deep integrations already built for real-world deployment. This approach reduces risk and cost while giving you the flexibility to scale with confidence.

Choosing between these paths is not always a binary decision. In many cases, the optimal strategy is a hybrid model that combines the customization benefits of in-house development with the speed, scalability, and stability of a proven AI partner like Artera.

Lessons Learned

We’ve spent years investing in AI, and with the rapid rise of Agentic AI, we’ve quickly embraced, configured and deployed it for many of our customers. Here’s what we’ve learned along the way…

Technology Evolution Speed

The pace of change in Agentic AI is unprecedented. Technologies that required significant custom development just months ago are now available as standard features from major providers. This rapid evolution makes long-term technology investments particularly risky for organizations building in-house solutions.

Security and Compliance Complexity

Agentic AI systems are advancing at a rapid pace, requiring a fundamental shift in how we approach data security. Traditional static, point-in-time assessments are no longer sufficient. Instead, safeguarding these dynamic systems calls for continuous monitoring, robust multi-layered security frameworks, and a seamless integration of human oversight with AI-driven validation.

Real-World Deployment Value

The competitive advantage lies not in the underlying AI technology itself, but in healthcare-specific implementation experience. Understanding how to handle edge cases, manage patient interactions, and integrate with healthcare workflows requires extensive domain expertise.

Cost Structure Reality

The current pricing for AI solutions is unsustainable at scale. Similar to the dot-com boom, the current market is subsidized, with artificially low AI prices. When the inevitable market correction happens, many vendors won’t survive. Therefore, organizations must partner with vendors that are not only financially stable enough to withstand the crash but also have sustainable business models for long-term success.

Looking for more? Join the 12/9 healthsystemCIO webinar, Maximizing Value, Minimizing Risk: Operationalizing an Agentic AI Strategy, to discuss this topic more.

Today’s healthcare market is saturated with AI agent solutions, making vendor evaluation difficult for healthcare providers amidst similar claims and significant costs.

To simplify your evaluation, we’ve identified the top five factors that distinguish Artera’s AI agents today. Whether you’re new to AI agents or well into your research for a partner, we hope this distillation proves valuable.

Artera’s blog posts and press releases are for informational purposes only and are not legal or business advice. Artera assumes no responsibility for the accuracy, completeness, or timeliness of blogs and non-legally required press releases. Claims for damages arising from decisions based on this release are expressly disclaimed, to the extent permitted by law.

The post Agentic AI for Healthcare: Build, Buy or Partner appeared first on Artera.

Organizations are quickly adopting AI agents for healthcare to streamline operations, reduce staff workload and enhance the patient experience. But here’s the thing: the success of these tools comes down to one big factor: how well they integrate with your existing systems. Even the smartest AI is only as good as its ability to connect to your data and workflows. Without seamless integration, you risk expensive technology that doesn’t fully deliver.

The tricky part? Every vendor claims to offer “deep EHR integration” or “seamless connectivity.” For healthcare IT leaders, the challenge is cutting through the buzzwords to figure out which partner can deliver real results. Let’s break down what to look for when assessing an AI agent partner based on actual integration depth and scope. 

Integration Capabilities Impact AI Agent Success

AI agents need access to the right data to truly be effective. Think EHRs, scheduling tools, and billing platforms: these are the core systems running patient care. Real-time data access allows AI agents to make smart decisions based on current patient information, schedules, and protocols. They also need to communicate back, updating records, booking appointments, and triggering workflows.

If the integration isn’t solid, you’re left with fragmented data and manual workarounds, which defeats the purpose of automation. To get the most out of AI, you need a partner who can connect all the dots seamlessly.

Evaluating Integration Scope and Diversity

Health system leaders must thoroughly assess and understand the integration capabilities available from a potential agentic AI partner. When evaluating vendors, healthcare organizations should look beyond broad integration claims and marketing to truly grasp the depth of their offerings.

Effective integration demands careful consideration of several critical dimensions, including experience and distribution, the breadth and diversity of EHR integration types and overarching ecosystem strategies.

Below are several topics and questions to consider when evaluating an agentic AI vendor’s integration capabilities across these dimensions: 

Customer Volume and EHR Integration Diversity

Many vendors can showcase one or two basic integrations, but scaling AI agents across healthcare teams and environments demands proven expertise with multiple data sources. It’s important to seek out vendors who can demonstrate experience with multiple customers using your EHR platform, the ability to handle diverse data feed types beyond basic scheduling, and production workflows that actively process patient interactions.

Potential Question to Ask Vendors During Evaluation:

“How many customers have you successfully integrated with our specific EHR system, and across how many different data feeds / sources?”

The Artera Difference → At Artera, we maintain a comprehensive internal repository of EHR data feeds and integration types, which enables efficient knowledge transfer across teams and allows us to quickly reference EHR integration setups for customer support and shared learning.

Diverse Integration Methods

EHR integration isn’t a one-size-fits-all solution. It’s essential to partner with a vendor who has a thorough understanding of various integration methods and can guide your team toward the approach that best aligns with your needs. Choosing a vendor limited to a single integration method could restrict your options and hinder your success.

For example, static file uploads may be suitable for infrequently changing data, such as FAQ content, but dynamic patient information requires real-time API connectivity. Vendors should demonstrate understanding of when to use different integration approaches and the operational implications of each choice.

Question to ask: 

“Do you support multiple integration pathways, and can you give examples of when each was used successfully? How do you determine the right approach for different data types?” 

The Artera Difference →At Artera, our integrations extend across multiple data feeds and API endpoints. Our in-house integration engineering team supports the full spectrum of healthcare data exchange methods, including:

• HL7 messaging for real-time clinical data exchange
• Flat file for structured files like CSV, TXT or TSV
• FHIR APIs for modern, standards-based connectivity
• SFTP file transfers for batch data processing
• Custom development for unique organizational requirements

While these technical methods are crucial for data exchange, the next-generation challenge is making that data actionable for AI agents. To reliably enable core agent functions—such as scheduling appointments, updating patient records, or processing prescription refills—the industry is adopting Model Context Protocol (MCP) as a best practice. MCP acts as a standardized “translation” layer, allowing Large Language Models (LLMs) to speak fluently with your existing clinical and operational systems. This open standard ensures that the AI agent’s interactions and transactions are consistent, trustworthy, and scalable across all your healthcare workflows.

For an even deeper understanding of MCP, read this blog from Artera alum and technical advisor Ashu Agte.

Workflow Support

Beyond a strong ecosystem, assess the complexity of workflows the vendor can handle, and their ability to support diverse teams and functions across an entire health system. Does the vendor’s solution only assist with scheduling (i.e., just in the call center), or can it manage a broader range of healthcare workflows across different teams?

The more comprehensive the workflows a vendor can support, the better equipped they are to scale alongside your organization’s evolving needs across departments and teams.

Questions to ask:

 “What use cases do you support along the patient journey? Do you solve for one workflow, or can you expand to more complex use cases?” 

The Artera Difference →At Artera, we work with customers across various areas and departments, including patient access and intake, value-based care, and chronic condition management teams. Our flexible use cases and workflow solutions allow us to scale and adapt to meet the unique needs of your organization. While some AI agent vendors focus on single-use cases like reducing call volume, Artera’s integration engine supports comprehensive patient journey workflows:

• Patient demographics and appointment management for personalized interactions
• Scheduling APIs for real-time appointment booking and modifications
• Referral management to reduce leakage and improve care coordination
• Post-discharge follow-up to ensure continuity of care
• Recall and preventive care campaigns for population health management
• And more…

Ecosystem & Partnerships 

An AI agent’s value multiplies when it can interact with your broader tech ecosystem. Vendors with established marketplace partnerships can unlock new workflows faster than those building relationships from scratch. 

Question to ask: 

“What existing partnerships / contracts do you have in place? How quickly can you enable integrations with our other technology vendors?

The Artera Difference → Beyond EHR integration, Artera’s Marketplace provides pre-built connections to 50+ digital health vendors across the care continuum. These established partnerships enable AI agents to access data and trigger actions across:

• Revenue cycle management platforms for billing and payment workflows
• Patient engagement tools for unified communication experiences
• Population health solutions for comprehensive care management
• Telehealth platforms for virtual care coordination
• Given the established partnerships we have with other vendors, we can unlock more workflows faster for our customers.  

What Questions Should You Ask When Evaluating Agentic AI Partners for Healthcare?

Here’s a quick list of questions to guide your conversations with potential agentic AI partners: 

What Are the Hidden Costs of Poor Integration?

Choosing an AI agent vendor with insufficient integration depth can result in serious downsides:

First, custom development expenses quickly accumulate when vendors lack pre-built connectors for your existing systems. Second, operational overhead can increase significantly, as staff must manually transfer data between disconnected platforms, wasting valuable time and resources. Third, scalability limitations emerge, as vendors may struggle to adapt their solutions to your organization’s evolving growth and changing needs.

Perhaps most importantly, weak integration prevents AI agents from accessing the full context required for smart decision-making. This directly reduces their effectiveness and hinders user adoption, undermining the very purpose of implementing AI.

Artera’s Integration Advantage

With over 10 years of healthcare-specific integration experience, Artera brings unmatched expertise to the AI agent space. We’ve supported 1,000+ healthcare organizations with EHR integrations across a variety of workflows, giving us the knowledge and tools to deliver robust, scalable solutions. For more information on our integrations, click here. 

Making the Right Choice for Long-term Success

Picking the right AI agent partner starts with understanding your goals. What challenges are you trying to solve? What future needs do you anticipate? Your vendor should not only address today’s issues but also scale with your growth.

As you evaluate potential partners, ask yourself one critical question: “Can this vendor truly support my organization’s long-term success?” If the answer is yes, you’re on the right track.

Today’s healthcare market is saturated with AI agent solutions, making vendor evaluation difficult for healthcare providers amidst similar claims and significant costs.

To simplify your evaluation, we’ve identified the top five factors that distinguish Artera’s AI agents today. Whether you’re new to AI agents or well into your research for a partner, we hope this distillation proves valuable.

Artera’s blog posts and press releases are for informational purposes only and are not legal or business advice. Artera assumes no responsibility for the accuracy, completeness, or timeliness of blogs and non-legally required press releases. Claims for damages arising from decisions based on this release are expressly disclaimed, to the extent permitted by law.

The post Agentic AI Healthcare Integration: How to Choose the Right Partner appeared first on Artera.

These are fundamentally simple tasks that create bottlenecks in the system. Patients face long hold times, high abandonment rates, and limited access to care coordination outside business hours. Meanwhile, healthcare staff spend valuable time on routine tasks instead of focusing on higher-acuity patient needs.

Patient communication isn’t just a part of patient access; it’s the foundation of it. When we remove communication barriers, we dramatically improve access to care. 

I believe agentic AI is poised to accelerate the breakdown of common patient communications barriers – faster and more effectively than ever before.

Agentic AI: A Transformative Force for Patient Communications

Agentic AI represents the next major tidal wave hitting healthcare. Hundreds of companies are entering this space, with hundreds of millions of dollars being raised. The pace of change is extraordinary—faster than any technology revolution we’ve experienced in our lifetimes, including mobile, web, and social media.

We’re seeing new infrastructure upgrades that meaningfully advance the technology’s potential on what feels like a daily basis. This rapid innovation is driving improvements across the board—from reducing latency to improving background noise suppression. 

At Artera, we’re deeply committed to driving innovation in agentic AI. Over the past few months alone, we’ve updated our underlying infrastructure more than 20 times, achieving exponential improvements to product quality. Our latency now sits below 500 milliseconds, we’ve deployed dozens of MCP tools and servers, our continuous learning framework is in production and ingesting live transcripts, and we’re supporting multiple languages. These aren’t just incremental improvements—they’re transformational leaps that directly benefit patient care.

Deterministic AI Agents: A Stepping Stone to Autonomous AI Agents 

Despite significant technological advancements, our core mission has remained unchanged for a decade: making healthcare number one in customer service. To us, “customer service” refers to how patients experience care outside the four walls of a hospital or clinic.

Our focus has been specifically on communications—enabling seamless, asynchronous interactions between healthcare providers and patients, no matter where they are. The rise of artificial intelligence has fundamentally changed how this experience will unfold in healthcare moving forward.

Our AI-powered, deterministic Flows Agents deliver two strategic benefits that align with our vision: 

First, it serves as a definitive knowledge base of proven pathways that have been hardened over many years across hundreds of institutions and for millions of patients. This knowledge repository is an invaluable tool for training fully autonomous agents.

Second, Flows Agents act as a stepping stone into fully autonomous AI. Healthcare operates under strict regulations, where technology—especially autonomous AI—can have life-and-death implications. Flows uses deterministic logic combined with natural language understanding (NLU) to guide patients through specific automated journeys. These pathways eliminate risks associated with hallucinations and jailbreaking, helping build initial comfort with AI among healthcare leadership, while creating a clear path toward full autonomy through our AI Agents. 

Where to Start: Automating Routine Interactions That Create Barriers to Care

Agentic AI offers the opportunity to automate the routine administrative phone tasks I initially mentioned, while making them available 24/7 at a quality bar that approaches that of real humans. By reducing the burden of simple interactions, we elevate staff to focus on higher-acuity patient needs, reduce hold times and abandonment rates, and ultimately improve access to care.

As system interoperability continues to advance, we’ll be able to streamline more routine patient tasks, creating a truly personalized, concierge-like experience for every patient while simultaneously reducing healthcare operating costs.

This enhanced accessibility will span all communication channels—voice, messaging, and web—ensuring patients can connect anytime, anywhere. At Artera, we’re excited to shape the future of patient communication, where every individual benefits from 24/7 concierge-like care.

The More Things Change, The More They Stay the Same

While technology is changing rapidly, the fundamental challenges of healthcare communication remain the same. Patients need frictionless, asynchronous communication channels that allow them to engage with their care teams on their own terms and schedules. Providers, on the other hand, need efficient, automated workflows with the flexibility to involve human intervention when necessary.

I believe our decade of experience in tackling these core challenges gives us a distinct edge as we innovate with agentic AI. By building on a foundation of proven solutions and deep market knowledge, we not only leverage the potential of technology but also address the real, pressing needs of the industry. 

Today’s healthcare market is saturated with AI agent solutions, making vendor evaluation difficult for healthcare providers amidst similar claims and significant costs.

To simplify your evaluation, we’ve identified the top five factors that distinguish Artera’s AI agents today. Whether you’re new to AI agents or well into your research for a partner, we hope this distillation proves valuable.

Artera’s blog posts and press releases are for informational purposes only and are not legal or business advice. Artera assumes no responsibility for the accuracy, completeness, or timeliness of blogs and non-legally required press releases. Claims for damages arising from decisions based on this release are expressly disclaimed, to the extent permitted by law.

The post Agentic AI Impact: A Tidal Shift in Healthcare Communication appeared first on Artera.

As AI agents become more sophisticated, the need for secure, structured communication between agents and systems has never been more important. Enter Model Context Protocol (MCP) – a new approach that’s redefining how AI agents interact with external systems while maintaining strict security boundaries.

While traditional APIs have served machine-to-machine communication well, they now fall short when it comes to agentic AI interactions. MCP fills this gap by providing a specialized protocol designed specifically for AI agents, complete with built-in security features that help prevent data spillage and reduce hallucinations.

What is Model Context Protocol (MCP)?

Model Context Protocol is a new standard for connecting AI models to external tools, data sources, and services, so they can more effectively communicate. Essentially, it functions as an API designed specifically for AI agents. Developed by Anthropic as an open source protocol in late 2024, MCP has quickly gained traction across the industry, despite being less than a year old.

Think of MCP as the evolution of how systems communicate. Where REST APIs and GraphQL handle traditional machine-to-machine interactions, MCP creates a structured pathway for agents to access system capabilities without compromising security or data integrity.

The protocol operates on a simple but powerful premise: instead of giving agents direct database access or unlimited system permissions, MCP creates a controlled interface that defines exactly what an agent can and cannot do. This approach fundamentally changes how we think about agent-system integration.

What are the Three Pillars of MCP?

MCP architecture consists of three core components that work together to create a comprehensive communication framework:

Pillar #1 Tools (The Agent’s Capabilities)

Tools represent the specific actions an agent can perform within a system. These are discrete functions that agents can call to interact with external services. Each tool has a defined scope and purpose. An agent cannot perform actions beyond its available toolset, creating natural boundaries around what’s possible during any interaction.

For healthcare applications, tools might include:

• Finding open appointment slots for rescheduling
• Booking new appointments
• Processing referrals
• Triaging patients to appropriate care levels
• Canceling or confirming existing appointments
• Escalating complex cases to human staff

Pillar #2 Resources: Static Information Repository

Resources encompass all the static information an agent needs to function effectively. This includes structured data like databases, documents, and reference materials that don’t change frequently. Resources provide agents with the contextual knowledge they need, without requiring real-time database queries for every piece of static information.

Common examples of resources include:

• Provider directories with doctor locations, specialties, and working hours
• Facility information like building locations and available services
• Parking and navigation details
• FAQ databases
• Policy documents and care recommendations

Pillar #3 Prompts: Contextual Communication Guidelines

Tied to the available resources and tools, prompts define how agents should respond in specific situations. They’re pre-written response templates that ensure consistent, appropriate communication based on the context of the interaction.

For instance, when an appointment scheduling tool returns no available slots, the associated prompt might guide the agent to say: “I couldn’t find any available appointments for your preferred time. Would you like to adjust your date range?”

This component ensures that agents maintain professional, helpful communication even when systems return unexpected results or errors.

These three pillars of MCP architecture (tools, resources, and prompts) create a comprehensive framework that addresses the key challenges of agent deployment: capability definition, information access, and response consistency. This structured approach not only enhances security but also improves the reliability and predictability of agent interactions.

Security Through Structure: How MCP Protects Data

One of MCP’s most significant advantages is its approach to security, which operates on multiple levels to protect sensitive information and prevent unauthorized access.

Hallucination Mitigation

Traditional agent implementations often gave AI systems direct database access, creating opportunities for hallucinations when agents generated plausible-sounding but incorrect information. MCP addresses this by normalizing data exchange and reducing ambiguity.

For example, when an MCP server receives a specific date from an agent, like “September 26, 2025,” (rather than sharing the numbers in a different order, such as 26-09-25), there’s little to no room for misinterpretation. The MCP can translate the data into its own specification for the agent, providing structured, verified responses, rather than constructing replies from raw database queries. This structured approach significantly reduces the likelihood of agents fabricating information (hallucinations).

The protocol also limits agents to only the information explicitly provided by the tools they call. If a tool is designed to verify patient appointments, it returns only verification status – nothing more. This prevents agents from accessing or inferring additional data beyond their designated scope.

Data Containment and Access Control

MCP creates strict boundaries around data access through its tool-based architecture. Agents can only access information through predefined tools, and each tool has specific parameters and return values.

This approach prevents data spillage in several ways:

• Limited scope: Tools only return the specific information they’re designed to provide
• No direct database access: Agents cannot make arbitrary queries or access raw data
• Structured responses: All information comes through controlled, formatted channels

If someone attempts to trick an agent into providing unauthorized information – like requesting a patient’s social security number – the agent simply has no tool capable of retrieving that data. The response would be: “I don’t have the capability to access that information. Would you like me to forward you to a human to answer that?”

Preventing Jailbreaking Attempts

Jailbreaking occurs when users try to manipulate agents into providing information or performing actions they shouldn’t. Classic examples include convincing an AI that harmful requests are actually for fictional purposes or creative projects.

MCP’s architecture makes jailbreaking significantly more difficult because agents physically cannot access information beyond their tool capabilities. Even if an agent were somehow convinced to attempt unauthorized data access, the underlying system simply doesn’t provide that pathway.

For healthcare applications, this is particularly crucial. Even if an agent hallucinates and generates a fake social security number or medical record number, that information isn’t sourced from actual patient data – it’s purely fabricated and can be detected and flagged by monitoring systems, like Judge LLMs.

The Future of Agent-System Communication

Model Context Protocol represents a fundamental shift in how we architect AI agent interactions. By providing structured, secure communication channels, MCP enables more sophisticated agent capabilities while maintaining strict security boundaries.

Many tech companies, including Artera, are already implementing MCP servers to integrate agent interactions with their platforms. This growing adoption suggests that MCP is on track to become a standard protocol across the tech industry, similar to how REST APIs became ubiquitous for web services.

While MCP shows great promise, we’re prioritizing security as the protocol continues to mature. For example, our MCP server operates within a controlled environment, accessible only to authorized agents, rather than being publicly available on the internet. 

As the protocol matures, we anticipate enhanced security standards, broader industry adoption, and more sophisticated toolsets that enable agents to handle increasingly complex workflows. I believe that organizations – such as Artera – which adopt MCP early are well-positioned to leverage these advances in agentic AI while maintaining robust security practices.

Today’s healthcare market is saturated with AI agent solutions, making vendor evaluation difficult for healthcare providers amidst similar claims and significant costs.

To simplify your evaluation, we’ve identified the top five factors that distinguish Artera’s AI agents today. Whether you’re new to AI agents or well into your research for a partner, we hope this distillation proves valuable.

Artera’s blog posts and press releases are for informational purposes only and are not legal advice. Artera assumes no responsibility for the accuracy, completeness, or timeliness of blogs and non-legally required press releases. Claims for damages arising from decisions based on this release are expressly disclaimed, to the extent permitted by law.

The post Model Context Protocol Explained: The Key to Agentic Healthcare appeared first on Artera.

As the SVP of Technical Operations at Artera, my mission is to uphold the highest standards of security while fostering a culture deeply rooted in data protection. Given the dynamic nature and rapid change of the agentic AI landscape, we have a unique opportunity today to ensure that our security protocols remain agile and resilient in the face of new challenges. If this past year has taught us anything, it’s that as AI agents become more advanced and independent, the risks of data breaches, hallucinations and leaks can escalate quickly. 

So, what does this mean from a security standpoint? Data security today – in this new era of agentic AI – requires a fundamental shift in strategy, and can no longer rely on static, point-in-time assessments. Instead, it demands continuous monitoring, multi-layered security frameworks and the integration of human oversight with AI-powered validation. 

Healthcare providers seeking agentic AI solutions need partners who truly understand this and have built robust security systems designed specifically with agentic AI in mind. 

Why HITRUST Alone Falls Short in the AI Era of Healthcare

Traditional frameworks like HITRUST are a solid starting point for protecting healthcare data, but they just can’t keep up with how fast agentic AI systems evolve. While HITRUST shows a commitment to safeguarding PHI, securing agentic AI requires a whole new approach.

Here’s the thing: agentic AI doesn’t play by the same rules. These systems are constantly learning, adapting and making decisions on their own. What worked yesterday might not work today, and something secure this morning could have vulnerabilities by the afternoon. A one-time security assessment just doesn’t cut it anymore – we have to be vigilantly guarding the way that AI is using our data. 

It gets trickier when you factor in how AI models get updated, retrained or tweaked between security reviews. Every change can bring new risks or behaviors that weren’t there before. Traditional frameworks simply don’t have the flexibility to keep up with these rapid changes, leaving organizations open to threats that didn’t even exist during their last compliance check.

Beyond Compliance: A Comprehensive Multi-Pillar Approach to Security

Just relying on HITRUST isn’t enough anymore. Working with vendors with multiple certifications gives you stronger, layered protection. That’s why leading health tech companies are choosing a mix of certifications to handle the dynamic nature of AI security.

I like to think of it as a jigsaw puzzle—each certification is a piece that shows how committed an organization is to keeping its systems safe and secure. Here are my “cliffnotes” on the different certifications we prioritize at Artera:

• HITRUST: the foundational layer for healthcare; demonstrates a commitment to safeguarding PHI 
• SOC 2 Type 2: third-party audit that highlights strong internal controls around data and systems – it’s a key signal of operational maturity for the business as a whole
• ISO 27001: general framework that provides the foundation for information security management systems in place
• ISO 27017: certification that specifically addresses cloud service security
• ISO 27018: certification that focuses on personally identifiable information (PII) protection in an organization’s environment
• ISO 27701: certification that covers privacy management and an organization’s commitment to keeping any privacy-related information confidential 

As you can see, each certification plays a different role. When these pieces come together, they create a multi-pillar approach to security. 

At Artera, we’re not just meeting these standards—we’re also pursuing FedRAMP High authorization, which is the Federal Risk and Authorization Management Program’s most rigorous security baseline for cloud services handling highly sensitive government data (in fact, Artera recently achieved “in process” FedRAMP High designation). 

So why does this matter? Pursuing FedRAMP High status reflects our commitment to the highest level of security protocols, elevating our approach to data protection and enhancing our understanding of the evolving security landscape. 

Security Considerations for Evaluating Agentic AI Partners

So, what security certifications should health system leaders focus on in this rapidly evolving agentic AI landscape? What questions should they ask their potential partners? Where should they focus their time? 

Beyond those certifications listed above, health system leaders should focus on three fundamental areas when assessing potential agentic AI vendors: data containment, spillage prevention and hallucination mitigation. 

These represent the most significant risks unique to AI systems, and require specialized approaches that traditional security frameworks don’t address.

In addition to the preventive measures mentioned, continuous monitoring and real-time alerts are essential while agents are active. 

Building a Culture of Security, Not Just Compliance

While no system is ever 100% secure, we can do a lot to protect ourselves by using every available safeguard and holding ourselves accountable. The goal is to keep both internal and external threats from compromising our systems. Just as important is having a clear audit trail so we can handle any issues that come up. Above all, we need to protect the healthcare data with all we’ve got. This includes fostering a culture of security and continuous improvement. 

At Artera, I’m proud to say that security isn’t just a checkbox or a compliance exercise. It’s a core business principle and vital investment. Over the past few years, I’ve witnessed a remarkable cultural shift within our organization. Security has become a collective effort embedded in everything we do.

I’ve observed a growing interest in security across teams, functions, and employees. Colleagues are asking insightful questions, actively expanding their knowledge, and sharing valuable security insights throughout the company. What stands out most is the heightened curiosity and engagement. It’s both inspiring and encouraging to witness this level of commitment.

Preparing for the Future of Agentic AI Security

As AI continues to play a bigger role in healthcare, keeping systems secure is only going to get more complicated and more important. The organizations that prioritize strong security partnerships now will be better positioned to take full advantage of AI’s benefits while keeping patients’ trust intact.

When choosing an agentic AI partner, it’s a good idea to focus on vendors who not only have solid security measures in place today but are also committed to staying ahead of future challenges. I encourage providers to look for vendors who stay on top of AI security trends, invest in research and innovation, and can quickly adapt to new threats with effective solutions.

Today’s healthcare market is saturated with AI agent solutions, making vendor evaluation difficult for healthcare providers amidst similar claims and significant costs.

To simplify your evaluation, we’ve identified the top five factors that distinguish Artera’s AI agents today. Whether you’re new to AI agents or well into your research for a partner, we hope this distillation proves valuable.

Artera’s blog posts and press releases are for informational purposes only and are not legal advice. Artera assumes no responsibility for the accuracy, completeness, or timeliness of blogs and non-legally required press releases. Claims for damages arising from decisions based on this release are expressly disclaimed, to the extent permitted by law.

The post Why HITRUST Certification Isn’t Enough for Agentic AI Systems: Insights from Artera’s SVP of Technical Operations appeared first on Artera.