AI Agents for Developers with Python and MCP: Build Production-Ready Agents with LangGraph, RAG, Tool Use, Multi-Agent Workflows, FastAPI, and AgentOp

By Lucian Verne

Building an AI agent may sound intimidating-but you do not need to be an AI expert to begin.

AI Agents for Developers with Python and MCP is a practical, step-by-step guide that shows you how to move from simple model calls to reliable, production-ready agent systems.

You do not need previous experience with AI agents, LangGraph, retrieval-augmented generation, MCP, multi-agent systems, FastAPI, observability, or deployment. Basic familiarity with Python is enough, and every major concept is explained before you use it.

Instead of overwhelming you with theory or enormous code listings, this book breaks complex systems into small, connected steps. You will build working components, test each stage, understand common failures, and gain confidence through visible progress. Mistakes are treated as a normal part of learning, while every completed test, successful tool call, grounded response, and resumed workflow becomes a practical win.

Key Features

Beginner-friendly explanations of modern AI-agent engineering

Step-by-step Python implementations with clear verification points

Five connected, real-world agent projects

Production-focused coverage of security, testing, monitoring, deployment, and recovery

Provider-neutral architecture that reduces dependence on a single model vendor

Practical references for commands, schemas, evaluation metrics, MCP, troubleshooting, and production readiness

What You Will Learn

Design agents with structured outputs, controlled tools, permissions, and failure handling

Build stateful AI workflows with LangGraph

Create RAG systems with document ingestion, vector retrieval, grounding, and citations

Build and connect MCP servers, clients, tools, resources, and prompts

Coordinate supervisor and specialist agents in multi-agent workflows

Add human approval, authentication, persistence, background workers, and audit records

Test and evaluate retrieval, tool use, model decisions, and agent behaviour

Expose agent workflows through FastAPI

Monitor, containerise, deploy, and operate a complete AgentOps platform

Who This Book Is For

This book is for Python developers, backend developers, students, self-learners, and technical professionals seeking a clear introduction to AI agents and production AI engineering.

It is especially valuable for readers who have experimented with language models but are unsure how to turn a promising demonstration into a dependable, testable, and deployable software system.

Table of Contents

From Model Calls to Production Agent Systems

Models, Structured Outputs, and Controlled Tool Use

Building Stateful Agent Workflows with LangGraph

Building the Evidence-Based Deep Research Agent

Building the Customer-Support Knowledge Agent

Understanding and Building with the Model Context Protocol

Building the MCP Business Operations Assistant

Building the Multi-Agent Market Intelligence System

Testing, Evaluating, and Governing Agent Behaviour

Building the Production Agent Runtime

Assembling the AgentOps Production Platform

Containerising, Deploying, and Operating the Platform

Stop treating AI agents as mysterious demonstrations. Start building them as reliable software systems.

Begin your production AI engineering journey today and turn basic Python knowledge into practical, deployable agent applications.

• Format: Paperback
• Language: English
• ISBN: B0H8WQ668K
• ISBN13: 9798187339600
• Release Date: July 2026
• Publisher: Independently Published
• Length: 400 Pages
• Weight: 1.52 lbs.
• Dimensions: 0.8" x 7.0" x 10.0"