Authored by Kinfey Lo, this article details building interoperable multi-agent AI systems using Microsoft’s Semantic Kernel and the A2A protocol, offering real-world architectural guidance and implementation insights for enterprise solutions.

Building Multi-Agent AI Solutions Using Semantic Kernel and the A2A Protocol

By Kinfey Lo

In the rapidly evolving landscape of AI application development, orchestrating multiple intelligent agents is now essential for creating enterprise-grade, sophisticated solutions. While individual AI agents excel at specific tasks, complex business needs often require coordination between specialized agents across different platforms, frameworks, or even organizational boundaries. This is where Microsoft’s Semantic Kernel and the Agent-to-Agent (A2A) protocol form a powerful foundation for constructing truly interoperable multi-agent systems.

Understanding the A2A Protocol: Beyond Traditional Tool Integration

Introduced by Google in April 2025, the Agent-to-Agent (A2A) protocol—supported by over 50 technology partners—addresses a key challenge: enabling intelligent agents to communicate and collaborate as peers, rather than simply function as tools. Unlike Model Context Protocol (MCP), which connects agents to external tools and data sources, A2A provides a standardized communication layer specifically built for agent-to-agent interaction.

Core A2A Capabilities

  1. Agent Discovery via Agent Cards: Every A2A-compliant agent makes available a machine-readable “Agent Card” (JSON document) that advertises its capabilities, endpoints, supported message types, authentication needs, and operational metadata. This enables dynamic discovery and selection of suitable agents for particular tasks.

  2. Task Management with Lifecycle Tracking: All interactions are structured as discrete “tasks” with defined lifecycles. Tasks can span from simple, immediate API calls to extended operations with real-time status updates.

  3. Rich Message Exchange: Agents communicate through structured messages, with parts supporting various content types—text, structured JSON, files, or even multimedia streams—enabling dynamic negotiation of interaction modes.

  4. Enterprise-Grade Security: Built on web standards (HTTP, JSON-RPC 2.0, Server-Sent Events), A2A incorporates enterprise authentication and authorization, including OpenAPI authentication schemes and secure collaboration without exposure of internal agent states.

A2A vs. MCP: Complementary Protocols

  • MCP: Connects agents to tools, APIs, data sources
  • A2A: Connects agents to other agents for peer collaboration and task delegation

These protocols often work together in sophisticated applications for broader interoperability.

Semantic Kernel: The Orchestration Engine

Semantic Kernel is Microsoft’s open-source SDK designed to:

  • Support a plugin-based architecture for extensible agent capabilities
  • Orchestrate multiple AI models for task specialization
  • Seamlessly integrate with enterprise systems and APIs
  • Enable multi-agent coordination (experimental)

Why Combine Semantic Kernel and A2A?

  • Framework-Agnostic Interoperability: Communicate with agents built using various frameworks (LangGraph, CrewAI, Google ADK, etc.)
  • Leverage Semantic Kernel’s Strengths: Benefit from SK’s plugins, prompt engineering, and enterprise features while obtaining cross-platform compatibility
  • Incremental Adoption: Easily integrate A2A into existing SK applications
  • Cloud-Native Design: Scalable operations with authentication, logging, and observability in mind

Architecture Patterns for Multi-Agent Systems

1. Centralized Routing with Azure AI Foundry

A central routing agent—powered by Azure AI Foundry—delegates tasks to remote, specialized agents.

Key Components:

  • Host Agent (central routing system using Azure AI Agents)
  • A2A Protocol (standardizes agent-to-agent communication)
  • Semantic Kernel (advanced orchestration with MCP integration)
  • Remote Agents (task-specific agents supporting different protocols)

Benefits:

  • Unified state management via Azure AI Foundry threads
  • Intelligent, capability-aware task delegation
  • Consistent user experience
  • Detailed auditing and robust error handling

2. Multi-Protocol Agent Communication

Supports coexistence of various protocols:

  • A2A HTTP/JSON-RPC for tool agents
  • STDIO for agents like Playwright automation
  • Server-Sent Events (SSE) for serverless MCP functions (Azure)

3. Hybrid MCP + A2A Integration

  • MCP: Handles tools, data, and development resources
  • A2A: Manages inter-agent collaboration and delegation
  • Semantic Kernel: Functions as the orchestration bridge

Implementation Deep Dive

Project Structure and Components

  • Modular architecture for scalability and maintainability
  • Components for host agents, specialized agents, plugins, and interfaces

Development Environment Setup

Sample setup steps:

# Initialize Python project

yarn init multi_agent_system
cd multi_agent_system

# Add essential Python dependencies

uv add semantic-kernel[azure]
uv add azure-identity
uv add azure-ai-agents
uv add python-dotenv
uv add a2a-client
uv add httpx
uv add semantic-kernel[mcp]
uv add gradio
uv add --dev pytest pytest-asyncio

Environment variables:

AZURE_AI_AGENT_ENDPOINT=https://your-ai-foundry-endpoint.azure.com
AZURE_AI_AGENT_MODEL_DEPLOYMENT_NAME=Your AI Foundry Model Deployment Name
PLAYWRIGHT_AGENT_URL=http://localhost:10001
TOOL_AGENT_URL=http://localhost:10002
MCP_SSE_URL=http://localhost:7071/runtime/webhooks/mcp/sse

Creating the Central Routing Agent

Key Python structure:

class RoutingAgent:
    def __init__(self):
        # ... initialize remote connections, Azure agent client, agent cards

    async def initialize(self, remote_agent_addresses: list[str]):
        # Discover agents and connect via A2A
        # Setup the Azure AI routing agent

    async def _create_azure_agent(self):
        # Create function-calling agent for intelligent routing

    def _get_routing_instructions(self) -> str:
        # Return context-aware guidelines for task delegation

Building Specialized Agents with MCP Integration

Implementation highlights:

  • Uses Semantic Kernel’s MCP plugins for tools like Playwright (web automation) or development tasks
  • Sets up agents with appropriate instructions, models, and communication patterns (STDIO, SSE)

Web Interface with Gradio

The multi-agent system exposes a Gradio-based chat interface where requests are routed, responses displayed, and different agents are invoked transparently:

async def get_response_from_agent(message, history):
    response = await ROUTING_AGENT.process_user_message(message)
    return gr.ChatMessage(role="assistant", content=response)

Deployment and Operations

  • Local development: Run MCP server (e.g., Azure Functions), remote agents, and the host agent separately; launch web interface locally
  • Production tips:
    • Deploy each agent as a microservice (Azure Container Apps)
    • Use Azure Service Bus for discovery/communication
    • Employ Azure Application Insights for logging
    • Secure endpoints with proper authentication

Real-World Example Scenarios

Web Automation:

User: “Navigate to github.com/microsoft and take a screenshot”

  • Routed to Playwright Agent via A2A, which automates browser and returns results

Development Workflow:

User: “Clone https://github.com/microsoft/semantic-kernel and open it in VS Code”

  • Routed to Tool Agent, which coordinates git clone and IDE operations

See the sample on GitHub

Future Considerations & Roadmap

  • Enhanced real-time streaming between agents
  • Expanded multimodal (audio, video) communications
  • Dynamic UX negotiation and improved client-driven methods
  • Deeper Azure AI Foundry, Copilot Studio, and enterprise system integrations
  • Ongoing evolution with strong community and industry support

Conclusion

Combining Semantic Kernel’s orchestration with the standardized A2A protocol enables the construction of scalable, secure, and interoperable multi-agent AI applications. This approach breaks down silos between AI frameworks, leverages existing investments, and positions solutions for future growth as standards and tooling mature.

Whether upgrading existing Semantic Kernel solutions or building new multi-agent systems from scratch, integrating these technologies offers a foundation for powerful and flexible intelligent enterprise applications.

Author: Kinfey Lo

Published on Semantic Kernel Blog

This post appeared first on “Microsoft DevBlog”. Read the entire article here