John Savill examines the trade-offs between Microsoft Cloud Platform (MCP) and the Agent-to-Agent (A2A) protocol in the context of AI applications, offering developers practical insights for decision-making.

Choosing Between MCP and A2A for AI Applications

In this session, John Savill provides a comprehensive discussion on when to use Microsoft’s MCP (Microsoft Cloud Platform), the A2A (Agent-to-Agent) protocol, or a combination of both for building AI and generative AI applications leveraging LLMs.

Key Topics Covered

  • Introduction to AI Applications: Overview of the challenges and design considerations when building with LLMs and generative AI components.
  • What is MCP?: Detailed segment on the Microsoft Cloud Platform—its core features, where it excels, and scenarios it solves.
  • Understanding LLMs and MCP: How MCP interacts with large language models in real-world deployments.
  • The Role of Agents and Agent Protocols: Explanation of how agents interact, the need for orchestration, and the emergence of the A2A protocol.
  • A2A Protocol Overview: Deep dive into the A2A protocol, including the structure and function of agent cards, task coordination, message passing, and artifact management between agents.
  • Combining MCP AND A2A: When and why you might need both platforms, and the architectural considerations to keep in mind.

Additional Resources

Conclusion

John summarizes when to use MCP, A2A, or both, helping viewers understand how to approach multi-agent systems and AI service integrations on Azure and within the broader Microsoft ecosystem.