AI Agents Foundations

Build AI Agents That Actually Work in Production

AI agents are moving beyond simple chatbots. They can reason, use tools, retrieve information, remember previous interactions, call APIs, and take action. But building an agent that works reliably in the real world requires much more than connecting an LLM to a prompt.

AI Agents: Foundations is the practical starting point for developers and software engineers who want to move from experimenting with AI to building production-ready AI agents with Python, LangChain, and LangGraph.

Instead of learning through isolated examples, you will build a complete agent step by step around the fictional NexaCore support platform. What begins as a simple Python prototype evolves into a fully structured system with tools, memory, retrieval, planning, error handling, observability, testing, security, and deployment.

What You Will Learn

• Understand how AI agents differ from traditional LLM applications
• Build your first AI agent with Python, LangChain, and LangGraph
• Connect agents to real tools, APIs, and external services
• Implement short-term and long-term memory
• Build Retrieval-Augmented Generation (RAG) systems for agents
• Design effective prompts and agent reasoning loops
• Handle errors, retries, and unexpected agent behaviour
• Add logging, tracing, observability, and testing
• Protect agents against prompt injection and other security risks
• Prepare an agent for real production deployment

The emphasis throughout the book is on engineering, not hype. You will see how the pieces fit together, why production agents fail, and how to design systems that are easier to debug, test, secure, and extend.

No previous LangChain, LangGraph, or LLM API experience is required. If you already know Python and want to understand how modern AI agents are actually engineered, this book gives you the foundation you need.

Start here if you want to stop simply using AI agents and start building them.

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