Agentic AI with MCP: Build Structured Multi-Agent Systems with Model-Text Protocol Servers, LangChain, and Large Language Models: 3 (Building Intelligent Agents: The Complete Framework Series)

Nathan Steele

ISBN 13: 9798287399009

Published by Independently published, 2025

About this Item

Seller Inventory # LU-9798287399009

Bibliographic Details

• Title: Agentic AI with MCP: Build Structured Multi-Agent Systems with Model-Text Protocol Servers, LangChain, and Large Language Models: 3 (Building Intelligent Agents: The Complete Framework Series)
• Publisher: Independently published
• Publication Date: 2025
• Language: English
• ISBN 13: 9798287399009
• Binding: Paperback
• Condition: New
• Dimensions: 17.78W x 1.57H x 25.4D centimeters 17.78 centimeters width by 1.57 centimeters height by 25.4 centimeters depth
• Weight: 481 grams

About this title

Synopsis

Agentic AI with MCP
Build Structured Multi-Agent Systems with Model-Text Protocol Servers, LangChain, and Large Language Models

Unlock the future of intelligent software systems with Agentic AI and Model-Text Protocols (MCP)—the new frontier of AI engineering.

This cutting-edge guide walks you through building structured multi-agent systems using LangChain, AutoGen, CrewAI, and LLM orchestration protocols. Whether you’re designing an autonomous assistant, coordinating agents for knowledge work, or scaling tools in production with FastAPI, this book offers a practical, professional blueprint grounded in real-world applications and 2025 best practices.

You’ll learn how to design agent workflows, implement shared memory and vector stores, coordinate tools and roles, prevent hallucinations and output leakage, and deploy agents at scale using cloud-native architectures. From MCP message schemas to role-based prompt engineering, every concept is backed with actionable examples and battle-tested implementations.

Inside You'll Discover:

• How to architect agentic systems using MCP schemas and protocol flows

• Integration of LangChain memory, AutoGen histories, and vector databases

• Task-based collaboration with CrewAI and function-calling agents

• Secure design practices: sandboxing, red teaming, audit logging, and oversight

• Deploying scalable agents with FastAPI, Docker, Fly.io, and background services

• Real-world use cases: AI developer assistants, legal PDF agents, alert triage bots, and more

• AgentOps: Evaluating and tuning agents in production environments

Who This Book Is For:

• AI developers and engineers working with LLMs and orchestration frameworks

• Backend and platform teams exploring multi-agent coordination

• Technical founders and automation specialists designing autonomous workflows

• Anyone building AI agents using LangChain, OpenAI, Claude, or custom APIs

Whether you're building a single-agent tool or coordinating fleets of autonomous workers, Agentic AI with MCP is your trusted guide to building reliable, secure, and scalable agent systems.