by Siddharaj Sarvaiya

July 14, 2025

Custom AI Agents for Healthcare: What Tech, Talent & Tools You Need

TL;DR

Custom AI agents are superior to off-the-shelf solutions for medical practices because they can be tailored to specific workflows like patient intake, claims processing, and prior authorizations, which ensures more accurate, secure, and efficient interactions. These agents integrate deeply with existing systems like EHRs and insurance APIs, offer continuity in conversations through memory layers, and adhere to healthcare compliance standards like HIPAA. By leveraging specialized models and frameworks, custom AI solutions deliver higher accuracy and scalability, making them essential for improving both patient experience and operational efficiency in healthcare settings.

Why are Custom AI Agents Better than Off-the-Shelf Solutions for Medical Practices?

Healthcare workflows are highly specific. Whether it’s patient intake, claims processing, or prior authorization, the data, logic, and handoffs are unique to every team.

Off-the-shelf healthcare AI agents usually stop at surface-level responses. They often miss critical pieces like checking a patient’s benefit eligibility across payers or knowing when to escalate a conversation to a live nurse.

That’s why clinical ops, product, and IT teams are exploring custom AI agents in healthcare.

What Tech Stack is Used to Build Custom AI Agents for Healthcare?

Here’s a clear breakdown of what goes into the tech stack, layer by layer.

1. Model (LLM) Layer

This is the thinking part of your AI agent. The model takes patient input, questions, or structured data and generates human-like responses.

Options you can work with:

✔️ GPT-4 or Claude via APIs – fast, reliable for early builds

✔️ Med-PaLM or BioGPT – good for clinical accuracy

✔️ Open-source models like LLaMA, Mistral – when you want on-prem deployment or more control

2. Memory & State Layer

If your healthcare AI agent forgets what the patient said two minutes ago, the whole experience falls apart. Memory helps it stay coherent across steps.

What you need:

✔️ Short-term memory for back-and-forth chat

✔️ Long-term memory for session history or task progress

3. Tool Use Layer

A smart AI agent should do more than talk. This layer connects your agent to the systems that make decisions or move tasks forward.

Common actions:

✔️ Run eligibility checks through insurance APIs

✔️ Submit prior auths or intake forms

✔️ Pull patient info from your EHR

✔️ Trigger appointment reminders

4. Agent Orchestration

If your use case spans multiple tasks like verifying coverage, then submitting forms, this layer keeps things organized.

What it handles:

✔️ Breaks big workflows into steps

✔️ Routes the right query to the right sub-agent

✔️ Handles fallback rules (like escalating to human staff)

5. Interface Layer

Your medical AI agent needs to live somewhere people can interact with it. The frontend layer is how users experience your agent – patients, billing staff, clinical teams, and anyone.

Where you can deploy:

✔️ IVR or voice systems for call centers (Twilio, Amazon Connect)

✔️ Chat widgets in your patient portal or app

✔️ Slack or Teams for internal billing or ops agents

✔️ EMR dashboards or clinical apps

6. Privacy, Security & Compliance Layer

Since you’re dealing with PHI, this layer matters as much as the model itself. Every interaction should meet healthcare compliance requirements.

What to include:

✔️ PHI redaction and encryption

✔️ Prompt and response logging for auditability

✔️ Role-based access to model features

✔️ Monitoring and version control of prompt logic

7. Data Retrieval & Vector Search

Sometimes, your healthcare AI agent needs to answer based on internal documents like payer contracts, SOPs, or clinical guidelines.

What makes this work:

✔️ A vector database that stores content in an AI-friendly format

✔️ A retrieval system that pulls the right context when a user asks something

Who Builds Custom Healthcare AI Agents and What Roles are Involved?

HTML Table Generator

Role	Core Responsibilities	Where They Fit in AI Agent Development
AI/ML Engineer	- Selects and fine-tunes the right language model - Adds domain-specific guardrails and behavior tuning - Evaluates accuracy, response quality, and edge-case handling	Early phase (model design and logic), ongoing tuning, and validation
Prompt Engineer	- Designs and tests prompt templates and flows - Implements few-shot examples and retrieval augmentation - Builds logic for chaining, fallback, and escalation	Throughout the agent's lifecycle, especially in functional design and error handling
Backend/Integration Engineer	- Connects the agent to EMRs, CRMs, billing, scheduling, and clinical databases - Uses FHIR, HL7, REST, or GraphQL to interface securely - Manages authentication and session management	Core to making the agent functional across your systems
Clinical SME (Subject Matter Expert)	- Provides workflow inputs, validates outputs - Defines escalation criteria, error boundaries, and acceptable variations - Helps test and refine task logic (e.g., symptom triage, order entry)	Early-stage requirements, ongoing testing, and tuning
MLOps Engineer	- Manages model deployment pipelines - Monitors performance, drift, and latency - Adds observability, rollback mechanisms, and autoscaling	Post-development and production rollout
DevSecOps / Platform Engineer	- Manages hosting, API gateway, and containerization - Implements HIPAA/GDPR/MDR-aligned access control and audit logging - Encrypts PHI in transit and at rest	Infrastructure setup and post-launch scaling
AI Compliance/Governance Lead	- Sets risk thresholds and human-in-the-loop policies - Prepares documentation for HIPAA, MDR, or FDA alignment - Oversees policy guardrails and consent flows	Mid-to-late stages, especially for scaling or public release
UX/Conversation Designer (Optional but valuable)	- Designs tone, flow, fallback messages - Creates empathetic phrasing for chat or phone - Tests user journeys for comfort and clarity	User-facing layer (esp. in phone/chat interfaces)

You don’t need to hire all these roles in-house.

AI agent development service providers like Azilen Technologies step in with ready teams who’ve already worked in healthcare workflows.

Not Every AI Agent Fits Healthcare

We build the ones that do – safely, fast, and for real use cases.

Explore AI Agents Development Services

What Tools are Used in Building AI Agents for Medical Use Cases?

The right tools can save weeks or months during healthcare AI agent development, especially if you plan to scale later. Here are tools that have proven helpful:

Agent Frameworks (To Orchestrate Tasks and Logic)

LangChain, CrewAI, Haystack: Let you manage multi-step agents, route tasks between sub-agents, and handle tool use (such as API calls or data retrieval).

Explore features, benefits, and use cases of top AI agent frameworks.

Prompt Management and Testing

PromptLayer, Rebuff, Helicone: Help version, monitor, and debug prompts as your workflows evolve. Especially useful in compliance-heavy environments.

Vector Databases (For Knowledge Retrieval)

Pinecone, Weaviate, Milvus, Qdrant: Store internal documents, FAQs, SOPs, or clinical guidelines. The agent can search this data to give better answers.

Security & Compliance Toolkits

Monitaur, Credo AI, PrivateGPT wrappers: Support audit logging, access control, and compliant data masking for HIPAA, GDPR, or MDR alignment.

Integration & API Middleware

Postman, Apideck, FHIRworks-on-AWS: These help connect the agent to EHRs, CRMs, payers, or scheduling systems using secure healthcare-friendly APIs.

Monitoring & Analytics

Arize, WhyLabs, OpenLayer: Track performance, detect model drift, monitor latency, and surface failures in production.

Model Hosting & Infrastructure

Azure OpenAI, AWS Bedrock, Hugging Face Inference Endpoints, Modal: Options for running LLMs in compliant cloud environments or on-prem setups.

DevOps & MLOps for Deployment

Weights & Biases, MLflow, Prefect: These tools manage model lifecycle, deployment pipelines, and rollback capabilities.

AI Agents are Only as Strong as the Ops Behind Them

Modernize AI workflows with MLOps

Explore MLOps Services

In-House vs Development Partner: What to Consider

Here’s a quick comparison that may help your team decide how to move forward with a custom AI agent for healthcare.

HTML Table Generator

Scenario	In-House Makes Sense	Partnering Works Better
You already have a full AI team.	✅
You need to go live in 60-90 days.		✅
Your workflows require integration with legacy systems.		✅
You want external help with compliance alignment.		✅
You’re building a one-off agent for internal testing.	✅
You’re scaling to patient-facing use cases.		✅

Wondering What it Costs to Build a Custom AI Agent?

Read this blog to get your answers.

AI Agent Development Cost

How to Start Building Your Custom Healthcare AI Agent?

Start simple. Pick one use case. Maybe it’s reducing call volume. Or automating eligibility checks. Or helping providers with charting.

Once that’s clear, list the systems it needs to integrate with – EHR, billing, CRM, scheduling, etc.

From there, it’s about designing prompts, connecting APIs, and training the agent with the right data and rules.

Whether you want to lead the build or partner with a team like Azilen, clarity upfront saves a lot of time later.

Thinking of Building Your Own Custom AI Agent?

You already know the pain points in your workflows. We help you turn those into production-ready AI agents – compliant, connected, and contextual.

Let’s talk about your use case and what it would take to build it.

Already Seeing Use Cases in Your Workflows?

Get a tailored roadmap and cost estimation.

Schedule a Consultation

Related Insights on AI Agents

1. AI Agent Architecture

2. Top AI Agent Builders

3. Agentic AI Guide

4. Agentic AI Development

5. AI Agentic Workflows

6. Top Agentic AI Companies

7. Agentic AI vs Generative AI

8. Agentive AI Guide

Top FAQs on Custom AI Agents for Medical Practices

1. Why should I choose a custom AI agent instead of an off-the-shelf solution?

Custom AI agents are tailored to your specific workflows, such as patient intake, claims processing, and appointment scheduling, ensuring smoother operations and more accurate outcomes. Off-the-shelf solutions are limited in scope and may not integrate well with your unique systems like EHRs or insurance platforms, leading to inefficiencies and missed opportunities for automation.

2. Will a custom AI agent be able to handle sensitive patient information securely?

Yes, custom AI agents are designed with robust privacy and security measures to protect sensitive patient information (PHI). They comply with HIPAA and other regulations, offering encryption, access control, audit logging, and data redaction, ensuring that all interactions are secure and compliant with healthcare standards.

3. How long does it take to implement a custom AI agent in my medical practice?

The implementation timeline depends on the complexity of your needs. Simple use cases, like automating patient intake or appointment reminders, can be deployed within 60-90 days. More complex workflows that require deeper integration with existing systems (e.g., EHR, insurance APIs) may take longer.

4. What are the key systems that a custom AI agent can integrate with?

A custom AI agent can integrate with your practice’s existing systems, including Electronic Health Records (EHR), CRM systems, scheduling tools, billing platforms, and insurance APIs. This ensures that the AI agent can access the right data at the right time to support tasks like eligibility checks, form submissions, and patient communication.

5. How much does building a custom AI agent for healthcare cost?

Costs can vary based on the complexity of your use case and the systems that need to be integrated. Simple agents can be more affordable, while highly customized AI solutions involving multiple systems and compliance features will require a larger investment. It’s important to align the cost with the efficiency and time-saving benefits the agent will bring to your practice.

Glossary

1️⃣ Custom AI Agent: A purpose-built software agent powered by AI that is tailored to your specific healthcare workflows, systems, and rules.

2️⃣ LLM (Large Language Model): A type of AI model trained on large amounts of text. It can understand and generate human language.

3️⃣ FHIR: A healthcare data standard that makes it easier to access and share medical information between systems like EHRs, billing platforms, and patient apps.

4️⃣ Vector Database: A specialized database that stores and retrieves data based on meaning, rather than exact matches.

5️⃣ Prompt Engineering: The skill of crafting the right instructions or inputs for an AI model to make it respond correctly.

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Siddharaj Sarvaiya

Program Manager - Azilen Technologies

Siddharaj is a technology-driven product strategist and Program Manager at Azilen Technologies, specializing in ESG, sustainability, life sciences, and health-tech solutions. With deep expertise in AI/ML, Generative AI, and data analytics, he develops cutting-edge products that drive decarbonization, optimize energy efficiency, and enable net-zero goals. His work spans AI-powered health diagnostics, predictive healthcare models, digital twin solutions, and smart city innovations. With a strong grasp of EU regulatory frameworks and ESG compliance, Siddharaj ensures technology-driven solutions align with industry standards.