Pure Java Implementation of Agent Communication Protocol

ACPJava is pure Java implementation of Agent Communication Protocol. ACP adopts a RESTful architecture implemented over HTTP, supporting both synchronous and asynchronous agent interactions. It represents a significant step toward standardizing how AI agents communicate in enterprise environments. Building a multi-agent system with agents shared across teams and organizations can be challenging. You may need to write custom integrations each time a team updates their agent design or changes the agent’s framework. The Agent Communication Protocol (ACP) is an open protocol that addresses this challenge by standardizing communication between agents.

This article explores why creating a pure Java implementation of ACP , built on top of A2AJava, addresses critical gaps in the current ecosystem.

The Current State of Agent Communication Protocols
The agent communication landscape currently includes several protocols:

Model Context Protocol (MCP): The current phase emphasizes lightweight, standardized protocols such as MCP, ACP, ANP, and A2A. These protocols address previous limitations by enabling dynamic discovery, secure communication, and decentralized collaboration across heterogeneous agent systems
Agent-to-Agent Protocol (A2A): A2A is an open protocol developed by Google to standardize how AI agents communicate and exchange information, fostering a vibrant ecosystem of interoperable AI
Agent Communication Protocol (ACP): Agent Communication Protocol (ACP) is an open standard for agent-to-agent communication. With this protocol, we can transform our current landscape of siloed agents into interoperable agentic systems with easier integration and collaboration
Why Java Implementation Matters

1. Enterprise Ecosystem Dominance
   Java remains the backbone of enterprise systems worldwide. Most large organizations have significant investments in Java infrastructure, development teams, and operational expertise. A pure Java implementation of ACP removes barriers to adoption by leveraging existing enterprise capabilities.

2. Performance and Scalability Requirements
   Enterprise AI systems require robust performance characteristics. Java’s mature ecosystem provides proven solutions for:

High-throughput concurrent processing
Memory management at scale
JVM optimization for long-running services
Enterprise-grade monitoring and observability

1. Security and Compliance Enterprise environments demand stringent security controls. Java’s established security model, combined with Spring Boot’s enterprise security features, provides the foundation needed for production deployments in regulated industries.

A2AJava: The Foundation Layer
A2AJava is a Java implementation of Google’s Agent-to-Agent (A2A) protocol, designed to enable seamless communication and collaboration between AI agents. This framework provides a standardized way for AI agents to discover each other, share capabilities, and work together on complex tasks.

The choice to build ACPJava on top of A2AJava is strategic for several reasons:

Cross-Protocol Compatibility
A Java-first library designed to: ✅ Build MCP-compliant servers and tools while also supporting A2A protocol. This cross-protocol support means that ACPJava agents can communicate with:

Google A2A ecosystem agents
MCP-compliant tools and servers
ACP-standardized agents
Proven Architecture
A2AJava is a pure Java implementation designed to support both the Google A2A (Agent-to-Agent) protocol and the MCP (Model Context Protocol), making it a versatile framework for building agentic applications. Building on this proven foundation reduces development risk and accelerates time to market.

Real-Time Communication
Integration: Ensures real-time, efficient communication · Cross-Protocol Support: A2AJava natively supports both A2A and MCP in the same codebase, allowing developers to build applications where agents can communicate with each other. This capability is essential for responsive agentic systems.

ACPJava Architecture Benefits

1. Layered Design ACPJava implements a clean layered architecture:

Client Applications → ACP Protocol Layer → A2AJava Package → Tools4AI Framework → Spring Boot → JVM
This separation of concerns provides:

Clear abstraction boundaries
Testable components
Flexible deployment options
Maintainable codebase

1. Dynamic Agent Discovery The implementation leverages Tools4AI’s reflection capabilities to automatically generate ACP agent manifests from existing Java methods. This approach:

Reduces manual configuration
Ensures consistency between implementation and documentation
Enables rapid agent deployment
Supports dynamic capability discovery

1. Multiple Execution Modes ACPJava supports the full ACP specification:

Synchronous execution for immediate responses
Asynchronous execution for long-running tasks
Streaming execution for real-time interactions
This flexibility allows developers to choose the appropriate execution model based on their specific use case requirements.

Practical Implementation Advantages

1. Simplified Development Experience While the client can also be developed using frameworks like Angular or React (which I’ll cover in future articles), for now, we’ll focus on building the Java server for A2A. Once you create a Spring Boot project, the next step is to add a controller to handle JSON-RPC requests.

The Spring Boot integration provides familiar patterns for Java developers, reducing the learning curve.

1. Enterprise Integration ACPJava seamlessly integrates with existing enterprise Java infrastructure:

Spring Boot autoconfiguration
Standard Java logging frameworks
Enterprise monitoring solutions
CI/CD pipelines optimized for Java applications

1. Multi-Agent System Support This multi-agent system demonstrates how to effectively combine A2A and MCP protocols in a Java-based environment. The separation into Movies and Library servers showcases how different AI agents can be orchestrated while maintaining clean architecture and clear responsibilities.