AI Agents for Developers with Python and MCP: Build Production-Ready Agents with LangGraph, RAG, Tool Use, Multi-Agent Workflows, FastAPI, and AgentOp (Paperback) (ISBN-13: 9798187339600)

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.

• 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

• 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

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.

1. From Model Calls to Production Agent Systems

2. Models, Structured Outputs, and Controlled Tool Use

3. Building Stateful Agent Workflows with LangGraph

4. Building the Evidence-Based Deep Research Agent

5. Building the Customer-Support Knowledge Agent

6. Understanding and Building with the Model Context Protocol

7. Building the MCP Business Operations Assistant

8. Building the Multi-Agent Market Intelligence System

9. Testing, Evaluating, and Governing Agent Behaviour

10. Building the Production Agent Runtime

11. Assembling the AgentOps Production Platform

12. 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.