Building AI Agents
Extend Patient Journey Intelligence with custom AI agents using the Model Context Protocol (MCP), medical-grade LLMs/VLMs, and platform REST APIs.
Build Healthcare AI Agents in Hours, Not Months
Leverage pre-built medical tools, HIPAA-compliant infrastructure, and production-ready agent frameworks to rapidly develop domain-specific AI assistants.
Overview
Building healthcare AI agents traditionally requires:
- Months of infrastructure setup for HIPAA compliance, audit logging, and secure deployment
- Custom tool development for medical terminology, clinical calculations, and EHR integration
- Prompt engineering expertise for medical reasoning and clinical decision support
- Production monitoring for accuracy, safety, and regulatory compliance
The Building Agents framework eliminates these barriers by providing:
- Model Context Protocol (MCP) integration for standardized tool access
- Pre-built medical tools for clinical NLP, terminology lookup, and patient data access
- Production-ready LLM/VLM models fine-tuned on medical corpora
- Platform REST APIs for programmatic agent orchestration
Key Capabilities
MCP Agent Integration
Standardized Tool Access
- Connect to MCP-compliant tools and servers
- Automatic schema discovery and validation
- Built-in error handling and retries
- Tool composition and chaining
- Conversation state management
Pre-Built Medical Tools
Healthcare-Specific Capabilities
- Clinical NLP (NER, RE, assertion status)
- Medical terminology lookup (SNOMED, RxNorm, LOINC)
- Drug interaction checking
- Clinical guideline retrieval
- Patient data access (OMOP CDM queries)
Medical LLM & VLM
Domain-Specific Models
- LLMs fine-tuned on medical literature
- Vision models for radiology/pathology
- Specialized prompts for clinical reasoning
- Temperature and sampling control
- Response validation and safety filters
Platform REST API
Programmatic Access
- RESTful endpoints for all platform features
- OAuth 2.0 authentication
- Webhook support for async operations
- Batch processing APIs
- OpenAPI/Swagger documentation
HIPAA Compliance
Built-In Security
- Automatic PHI detection and redaction
- Audit logging of all agent actions
- Encrypted data at rest and in transit
- Role-based access control (RBAC)
- Compliance reporting and attestation
Monitoring & Analytics
Production Observability
- Real-time agent performance metrics
- Token usage and cost tracking
- Error rate and latency monitoring
- User satisfaction scores
- A/B testing for prompt optimization
Agent Development Workflow
From Concept to Production
Define Agent Scope
Identify the clinical task, required data sources, and expected outputs for your agent (e.g., "Draft discharge summaries from patient timeline data")
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Select Tools & Models
Choose from pre-built MCP tools (NLP, terminology, patient data) and medical LLMs/VLMs that match your use case requirements
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Develop Agent Logic
Write agent orchestration code using MCP SDK or REST APIs, defining tool call sequences and decision logic
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Test & Validate
Run agents on test datasets, validate clinical accuracy with domain experts, and iterate on prompts and tool selection
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Deploy to Production
Deploy agents to HIPAA-compliant infrastructure with automatic audit logging, monitoring, and rollback capabilities
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Monitor & Optimize
Track performance metrics, gather user feedback, and continuously improve agent accuracy and response quality
Example Agent Use Cases
📝 Clinical Documentation Assistant
Tools Used: NLP extraction, medical terminology, patient timeline API
Auto-generates progress notes, discharge summaries, and H&P documents from structured patient data and clinical notes
🔍 Diagnostic Support Agent
Tools Used: Clinical NLP, guideline retrieval, drug interaction checker
Analyzes patient symptoms and lab results to suggest differential diagnoses and evidence-based recommendations
📊 Quality Measure Calculator
Tools Used: OMOP CDM queries, clinical measures API, cohort builder
Automatically identifies eligible patients, extracts required data elements, and calculates HEDIS/CMS quality measures
🏥 Patient Triage Agent
Tools Used: Clinical NLP, medical LLM, patient data API
Reviews incoming patient messages and lab results to determine urgency and route to appropriate care team members
💊 Medication Reconciliation Assistant
Tools Used: RxNorm API, drug interaction checker, patient medication history
Compares patient medication lists across care settings to identify discrepancies and potential adverse interactions
🧬 Clinical Trial Matching
Tools Used: NLP extraction, OMOP cohort queries, external trial database APIs
Matches patients to relevant clinical trials based on diagnosis, stage, biomarkers, and eligibility criteria
Integration Options
🔌 Multiple Integration Paths
Build agents using the approach that best fits your tech stack:
- Model Context Protocol (MCP): Use Claude Desktop or other MCP-compatible clients to access tools via standardized protocol
- Python/JavaScript SDKs: Import our SDKs to build custom agents in your preferred language
- REST APIs: Make HTTP requests directly to platform endpoints for maximum flexibility
- Low-Code Agent Builder: Use visual workflow editor to compose agents without writing code
Security & Compliance
🔒 PHI Protection
All agent interactions automatically detect and handle PHI according to HIPAA Safe Harbor de-identification rules
📜 Audit Trails
Every agent action is logged with user ID, timestamp, input/output data, and model version for compliance and debugging
👥 Access Control
Role-based permissions ensure agents only access data and perform actions authorized for each user
✓ Model Validation
All medical LLMs undergo clinical validation and safety testing before deployment to production environments
Getting Started
Quick Start Guide
Review Pre-Built Tools
Browse the MCP Tools catalog to see available medical capabilities and their input/output schemas
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Set Up MCP Connection
Follow the MCP Agents guide to configure Claude Desktop or your MCP client to connect to platform tools
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Build Your First Agent
Start with a simple use case like extracting diagnoses from clinical notes using the NLP tool
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Explore Advanced Features
Learn about medical-specific models and REST APIs for production deployments
Best Practices
⚠️ Clinical AI Safety Guidelines
- Always validate outputs: AI agent responses should be reviewed by qualified clinicians before clinical use
- Use appropriate models: Select medical-specific LLMs for clinical reasoning; general models may hallucinate medical facts
- Handle uncertainty: Agents should explicitly state when confidence is low or information is missing
- Test on diverse data: Validate agent performance across different patient demographics, conditions, and documentation styles
- Monitor in production: Continuously track accuracy, error rates, and user feedback to catch performance degradation
- Document limitations: Clearly communicate what your agent can and cannot do to end users