AI-powered applications are rapidly becoming the new normal. Personal productivity assistants, coding agents, smarter search, and automated reporting tools are popping up everywhere. The LangChain ecosystem, and standards like MCP are driving this new gold rush. This book helps you claim your spot.
This is your hands-on guide to creating real, production-ready language model solutions. With LangChain and LangGraph, you'll orchestrate powerful agentic workflows and build dynamic tool-based agents that can search, summarize, reason, and act. You'll move from essential prompt engineering to advanced Retrieval Augmented Generation (RAG), and finally to deploying multi-agent systems using modern integration standards like the Model Context Protocol (MCP).
In AI Agents and Applications: With LangChain, LangGraph and MCP, you'll discover:
• Prompt and context engineering for accurate, hallucination-resistant systems
• Advanced RAG for summarization, semantic search, and reliable Q&A
• Structured, multi-step agentic workflows with LangGraph
• Tool-based agents that adapt in real time
• Multi-agent systems for complex, real-world tasks
• MCP integration to expose, compose, and consume plug-and-play tools
About the technology
This book teaches you to design reliable LLM-powered systems by focusing on the concepts, architectures, and design patterns that will stay stable even as models and APIs change. You'll learn to structure prompts, compose modular chains, and build RAG pipelines that ingest documents, split them into chunks, embed them, retrieve the right context, and ground answers to elliminate (or vastly reduce) hallucinations.
About the book
Along the way you'll build concrete applications?summarization and Q&A engines, context-aware chatbots with memory, and tool-using AI agents that orchestrate multi-step workflows with branching logic. For the examples, the book uses Python, LangChain, LangGraph, and LangSmith, but you'll be able to generalize to other frameworks. You'll understand with clarity and confidence how to keep integrations maintainable, manage context limits and cost/latency tradeoffs, and evaluate, debug, and monitor behavior so your systems work in production.
About the author
Roberto Infante is an AI innovator with deep FinTech experience, working for a London-based hedge fund. He specializes in building agentic systems for both plain vanilla and exotic quantitative analysis.
Table of Contents
Part 1
1 Introduction to AI agents and applications
2 Executing prompts programmatically
Part 2
3 Summarizing text using LangChain
4 Building a research summarization engine
5 Agentic workflows with LangGraph
Part 3
6 RAG fundamentals with ChromaDB
7 Q&A chatbots with LangChain and LangSmith
Part 4
8 Advanced indexing
9 Question transformations
10 Query generation, routing, and retrieval postprocessing
Part 5
11 Building tool-based agents with LangGraph
12 Multi-agent systems
13 Building and consuming MCP servers
14 Productionizing AI agents: Memory, guardrails, and beyond
Appendixes
A: Trying out LangChain
B: Setting up a Jupyter Notebook environment
C: Choosing an LLM
D: Installing SQLite on Windows
E: Open source LLMs
