Working with A2A Agents

A2A (Agent-to-Agent) agents are a new class of applications on Health Universe designed for AI-driven workflow automation. Unlike Streamlit and FastAPI apps that require direct user interaction, A2A agents can communicate with each other and operate autonomously within Navigator workflows, making them ideal for orchestrating complex healthcare processes.

What are A2A Agents?

A2A agents are conversational AI applications that:

• Process natural language messages and return structured responses

• Communicate with other agents to complete complex tasks

• Run autonomously as part of Navigator's AI-driven workflows

• Handle document operations like reading clinical files, generating reports, and updating records

• Provide progress updates during long-running tasks

Think of A2A agents as specialized AI assistants that can be chained together to automate entire clinical or administrative workflows.

A2A vs. Streamlit vs. FastAPI

Feature	A2A Agents	Streamlit	FastAPI
Primary Use	AI workflow automation	Interactive dashboards	API services
User Interaction	Natural language messages	Direct UI interaction	HTTP requests
Autonomy	Fully autonomous	User-driven	Request-response
Navigator Integration	Native AI workflows	Standalone apps	Tool orchestration
Document Access	Built-in file operations	Manual file handling	API-based access
Inter-app Communication	Native agent-to-agent	External integrations	API calls
Best For	Clinical workflows, automation	Data visualization, prototyping	Backend services, tools

When to Use A2A Agents

✅ Use A2A Agents when:

• Building AI-powered clinical workflows that need to process multiple documents or data sources

• Creating multi-step processes that involve several specialized tasks (e.g., document analysis → risk assessment → report generation)

• Developing autonomous assistants that can operate without constant user input

• Building agents that need to communicate with other AI agents in a workflow chain

• Creating background processing systems for tasks that take more than a few seconds

Example A2A Agent Use Cases:

• Clinical Documentation Assistant: Processes uploaded clinical notes, extracts key findings, and generates structured summaries

• Protocol Analyzer: Reviews research protocols, identifies potential issues, and suggests improvements

• Care Plan Generator: Takes patient data and clinical guidelines to create personalized care plans

• Quality Assurance Agent: Reviews clinical documentation for completeness and compliance

A2A Agents in Navigator Workflows

Navigator's AI engine can automatically:

1. Identify relevant agents based on the clinical context and user intent

2. Chain multiple agents together to complete complex workflows

3. Pass data between agents seamlessly

4. Monitor progress and handle errors across the entire workflow

5. Present results to users when the workflow completes

For example, a user might say "Analyze this patient's lab results and create a care plan," and Navigator could:

1. Route lab documents to a Lab Analysis Agent

2. Send the analysis to a Risk Assessment Agent

3. Have a Care Plan Generator create recommendations

4. Use a Documentation Agent to format the final report

Key Features of A2A Agents

Document Operations

A2A agents have built-in access to Health Universe's document system:

# List documents in the thread
docs = await context.document_client.list_documents()

# Read document content
content = await context.document_client.download_text(doc.id)

# Write new documents
await context.document_client.write("Analysis Results", json.dumps(results))

# Search documents semantically
results = await context.document_client.semantic_search("patient symptoms")

Progress Updates

Long-running agents can provide real-time progress updates:

await context.update_progress("Analyzing documents...", progress=0.3)
await context.update_progress("Generating recommendations...", progress=0.7)
await context.update_progress("Complete!", progress=1.0)

Inter-Agent Communication

Agents can call other agents programmatically:

# Call another agent in the workflow
response = await self.call_agent("protocol-analyzer", message, context)
result = response.text

Background Processing

A2A agents run as background jobs, allowing for:

• Long-running tasks (up to 1 hour by default)

• Persistent progress tracking stored in the database

• User notifications when tasks complete

• Error handling and recovery

Getting Started with A2A Agents

A2A agents are built using the Health Universe A2A SDK, which provides:

• Base agent classes for different use cases

• Built-in document operations for file handling

• Progress tracking and user notifications

• Agent-to-agent communication helpers

• Local development tools for testing

To start building A2A agents, you'll use the health-universe-a2a Python SDK, which handles all the complexities of the A2A protocol and Navigator integration.

Next Steps:

• Set up the A2A SDK

• Create your first agent

• Agent best practices

The Future of Healthcare Automation

A2A agents represent the next evolution of healthcare applications—moving from manual, user-driven tools to intelligent, autonomous systems that can handle complex workflows end-to-end. They enable healthcare professionals to focus on patient care while AI handles routine documentation, analysis, and administrative tasks.

By building A2A agents, you're contributing to a future where clinical workflows are more efficient, consistent, and intelligent, ultimately leading to better patient outcomes and reduced clinician burnout.