Overview

This book examines agent-to-agent communication using the Model Context Protocol (MCP), with a focus on structured interoperability and coordination across distributed AI systems. It begins with MCP server agent-to-agent interaction models and expands into protocol design considerations, message exchange patterns, and system-level coordination strategies. Rather than focusing on individual agents in isolation, the book emphasizes how multiple agents can be composed into coherent workflows using shared protocols and clearly defined interaction boundaries. Topics include MCP server roles, agent responsibility separation, context propagation, and integration with external services and tools. The discussion is grounded in production-oriented design concerns such as observability, fault tolerance, scalability, and long-term maintainability. The material assumes experience with distributed systems concepts, API-driven architectures, and modern AI development practices. This book is written for experienced engineers, system designers, and technical leads who are exploring structured approaches to multi-agent AI systems and require a protocol-centric perspective suitable for real-world deployment.

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