about this book
AI Agents and Applications is a hands-on, project-focused guide. We begin with the foundational skills that make large language model (LLM) applications work effectively in real-world scenarios: designing reusable prompts and grounding model outputs in your own data using Retrieval-Augmented Generation (RAG). From there, we progress into agentic workflows and multi-agent systems capable of using tools, making decisions, and collaborating when a single prompt is insufficient.
You’ll learn how to trace and debug your systems with confidence, transforming brittle prototypes into maintainable applications ready for deployment. We’ll use LangChain to build composable components, LangGraph to create clear and testable control flows (especially for agent-based solutions), and the Model Context Protocol (MCP) to integrate external capabilities as easily as local ones. Throughout the book, we’ll explore where these systems excel, where they fall short, and how to resolve common issues such as retrieval inconsistencies, imprecise queries, unreliable tool calls, and drifting behavior. The ultimate goal is straightforward: to help you build AI solutions your users can trust.