About the Book
Transform GenAI experiments into production-ready intelligent agents with scalable AI systems, architectural patterns, frameworks, and responsible AI and governance best practices
Key Features
Build robust single and multi-agent GenAI systems for enterprise use
Understand the GenAI and Agentic AI maturity model and enterprise adoption roadmap
Use prompt engineering and optimization, various styles of RAG, and LLMOps to enhance AI capability and performance
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionGenerative AI has moved beyond the hype, and enterprises now face the challenge of turning prototypes into scalable solutions. This book is your guide to building intelligent agents powered by LLMs.
Starting with a GenAI maturity model, you’ll learn how to assess your organization’s readiness and create a roadmap toward agentic AI adoption. You’ll master foundational topics such as model selection and LLM deployment, progressing to advanced methods such as RAG, fine-tuning, in-context learning, and LLMOps, especially in the context of agentic AI. You'll explore a rich library of agentic AI design patterns to address coordination, explainability, fault tolerance, and human-agent interaction. This book introduces a concrete, hierarchical multi-agent architecture where high-level Orchestrator agents manage complex business workflows by delegating entire sub-processes to specialized agents. You’ll see how these agents collaborate and communicate using the Agent-to-Agent (A2A) protocol.
To ensure your systems are production-ready, we provide a practical framework for observability using lifecycle callbacks, giving you the granular traceability needed for debugging, compliance, and cost management. Each pattern is backed by real-world scenarios and code examples using the open-source Agent Development Kit (ADK).What you will learn
Apply design patterns to handle instruction drift, improve coordination, and build fault-tolerant AI systems
Design systems with the three layers of the agentic stack: function calling, tool protocols (MCP), and agent-to-agent collaboration (A2A)
Develop responsible, ethical, and governable GenAI applications
Use frameworks like ADK, LangGraph, and CrewAI with code examples
Master prompt engineering, LLMOps, and AgentOps best practices
Build agentic systems using RAG, fine-tuning, and in-context learning
Who this book is forThis book is for AI developers, data scientists, and professionals eager to apply GenAI and agentic AI to solve business challenges. A basic grasp of data and software concepts is expected. The book offers a clear path for newcomers while providing advanced insights for individuals already experimenting with the technology. With real-world case studies, technical guides, and production-focused examples, the book supports a wide range of skill levels, from learning the foundations to building sophisticated, autonomous AI systems for enterprise use.
Table of Contents:
Table of Contents- GenAI in the Enterprise: Landscape, Maturity, and Agent Focus
- Agent-Ready LLMs: Selection, Deployment, and Adaptation
- The Spectrum of LLM Adaptation for Agents: RAG to Fine-tuning
- Agentic AI Architecture: Components and Interactions
- Multi-Agent Coordination Patterns
- Explainability and Compliance Agentic Patterns
- Robustness and Fault Tolerance Patterns
- Human-Agent Interaction Patterns
- Agent-Level Patterns
- System-Level Patterns for Production Readiness
- Advanced Adaptation: Building Agents That Learn
- A Practical Roadmap: Implementing Agentic Patterns by Maturity Level
- Use Case: A Single Agent for Loan Processing
- Use Case: A Multi-Agent System for Loan Processing
- Agent Frameworks: – Use Case: A Multi-Agent System for Loan Processing with CrewAI and LangGraph
- Conclusion: Charting Your Agentic AI Journey
About the Author :
Dr. Ali Arsanjani is the Director Applied AI Engineering at Google Cloud, and Head of GenAI Blackbelts and AI Center of Excellence, within the Google Cloud Product Engineering organization. Ali leads strategic pursuits for Cloud AI Partners and is the program manager for Google Cloud co-innovation and co-engineering programs that focus on adoption of Google AI for customers & partners through thought leadership, engagement, enablement and execution. Google's earliest adopters of GenAI have partnered closely with Dr.Arsanjani and his team on their use cases and adoption. He leads Generative and Agentic AI initiatives that enable customers and partners with the best-practices and tools that bring the capabilities of the Google Cloud AI platform to solve tactical and strategic challenges through best-practices and assets and achieving strong partnerships through strategic product co-innovation. Ali is an Adjunct Professor at San Jose State University and the University of California, San Diego, where he teaches and advises students in the Masters in Data program and the Data Science Institute, respectively. Prior to Google, Dr.Arsanjani was a Chief Principal Architect and WW Tech Lead at AWS, and previously CTO, Distinguished Engineer, Analytics and Machine Learning at IBM. Juan Pablo Bustos is a seasoned technologist with over 20 years of experience in driving innovation and delivering impactful solutions across diverse industries. Juan brings expertise in solution architecture, product incubation, and integration, coupled with expertise in cloud computing, AI, and machine learning.
