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AI Agents for Developers with Python and MCP: Build Production-Ready Agents with LangGraph, RAG, Tool Use, Multi-Agent Workflows, FastAPI, and AgentOp
Paperback
Series: Production AI Engineering, Book 1
Publisher: Independently Published
Published: Jul 14 2026
Pages: 400
Weight: 1.52
Height: 0.82 Width: 7.00 Depth: 10.00
Language: English
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 FeaturesBeginner-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.
Table of ContentsFrom 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.
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