Building AI Agents with A2A and MCP Protocol: A Hands-on Implementation Guide

The article focuses on building AI agents using A2A and MCP protocols in four different languages, deploying them, and setting them up with A2A and MCP clients as well as Java-based clients. A2A is primarily for agent-to-agent communication and coordination, while MCP is focused on agent-to-tool integration. The article demonstrates how to implement the same functionality across different JVM languages, showcasing the flexibility and interoperability of the A2A/MCP approach. The implementation includes creating a Spring Boot application with the EnableAgent annotation, creating a service as a normal Spring bean, and implementing the same functionality in other JVM languages such as Scala, Kotlin, and Groovy. The article also demonstrates how to add Spring-based security to agents, including role-based access control. The implementation includes creating a secure and modular AI-triggerable service that combines agentic design with Spring Security. The article also demonstrates how to build a complete server and client implementation in Java that works with both A2A and MCP protocols. The implementation includes building a server implementation, an A2A client implementation, and an MCP client implementation. The article also showcases how to scale an agentic system from individual task-specific servers to a fully coordinated multi-agent mesh.