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Full Description
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.
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
