Transform GenAI experiments into production-ready intelligent agents with scalable AI systems, architectural patterns, frameworks, and responsible AI and governance best practices Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key FeaturesBuild robust single and multi-agent GenAI systems for enterprise useUnderstand the GenAI and Agentic AI maturity model and enterprise adoption roadmapUse prompt engineering and optimization, various styles of RAG, and LLMOps to enhance AI capability and performancePurchase of the print or Kindle book includes a free PDF eBookBook 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 life cycle 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). *Email sign-up and proof of purchase required What you will learnApply design patterns to handle instruction drift, improve coordination, and build fault-tolerant AI systemsDesign systems with the three layers of the agentic stack: function calling, tool protocols (MCP), and A2A collaborationDevelop responsible, ethical, and governable GenAI applicationsUse frameworks such as ADK, LangGraph, and CrewAI with code examplesMaster prompt engineering, LLMOps, and AgentOps best practicesBuild agentic systems using RAG, fine-tuning, and in-context learningWho 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 tech