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Home > Computing and Information Technology > Computer science > Artificial intelligence > Expert systems / knowledge-based systems > Mastering Google Agent-to-Agent (A2A): Building Autonomous Multi-Agent AI Systems to Architect Scalable AI Workflows
Mastering Google Agent-to-Agent (A2A): Building Autonomous Multi-Agent AI Systems to Architect Scalable AI Workflows

Mastering Google Agent-to-Agent (A2A): Building Autonomous Multi-Agent AI Systems to Architect Scalable AI Workflows


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About the Book

Mastering Google Agent-to-Agent (A2A): Building Autonomous Multi-Agent AI Systems to Architect Scalable AI Workflows What if your AI systems could talk to each other like intelligent collaborators-passing tasks, sharing context, adapting in real time, and scaling without breaking down? That's the promise of Google's Agent-to-Agent (A2A) protocol, and this book shows you exactly how to harness it. Mastering Google Agent-to-Agent (A2A) is a hands-on developer's guide for building intelligent multi-agent systems using Google's Agent Development Kit (ADK), the Model Context Protocol (MCP), and Gemini-powered workflows. Whether you're orchestrating autonomous agents with Autogen, designing agentic systems in Python, or deploying next-gen AI pipelines using JSON-RPC and REST, this book gives you the practical tools and examples you need-fast. You'll learn to architect scalable AI workflows from the ground up, define robust MCP context envelopes, structure real-world agent communications using the Google ADK Agent Architecture, and deploy action-based, context-aware agents across hybrid cloud environments. This book includes step-by-step guidance on creating end-to-end AI agent pipelines, implementing Python examples for A2A and JSON-RPC endpoints, and using Gemini models in decision agents and tool-using frameworks. What makes this book different? It's structured for builders. Each chapter delivers focused lessons: Chapter 3: Creating Your First A2A Agent - Walks you through Python implementation and schema definition. Chapter 4: Context Management with MCP - Shows how to pass memory, goals, and persona across agent chains. Chapter 5: Secure Authentication - Covers OAuth2, role-based access, and credential strategies in multi-tenant setups. Chapter 6: Observability - Teaches distributed tracing, SLA monitoring, and structured logging. Chapter 7: Human-in-the-Loop Checkpoints - Adds approval UIs and auditability to your pipelines. Chapter 10: Real-World Use Case - Automate reports with n8n and A2A agents in a complete enterprise pipeline. Are you designing AI systems that need to scale without becoming unmanageable? Do you need to coordinate multiple LLM-based agents in production? This book gives you the architecture, the tools, and the confidence to do it right. Take your multi-agent AI development to the next level. Start building autonomous systems that communicate, adapt, and scale-efficiently, reliably, and with real-world impact. Buy the book now and start engineering intelligent pipelines with precision.


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Product Details
  • ISBN-13: 9798287412517
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 114
  • Returnable: N
  • Sub Title: Building Autonomous Multi-Agent AI Systems to Architect Scalable AI Workflows
  • Width: 178 mm
  • ISBN-10: 828741251X
  • Publisher Date: 09 Jun 2025
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Spine Width: 6 mm
  • Weight: 263 gr


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