AI Agent Engineer

This course contains the use of artificial intelligence.

Most tutorials show you how to make an LLM answer a question. This course shows you how to build an AI agent that can actually do things — call tools, remember context, ask for human approval, retrieve real data, and run reliably in production.

You'll go from the fundamentals of how AI agents work all the way to deploying a fully evaluated, secure, production-ready agent — using the same stack top AI teams use today: LangChain, LangGraph, and LangSmith.

In this course, you will:

• Understand how AI agents work and when (not) to use them

• Build agents with LangChain — chat models, structured outputs, and tool calling

• Design reliable agent architecture with guardrails and tracing

• Build stateful, multi-step workflows with LangGraph

• Add memory and human-in-the-loop approval to your agents

• Build RAG (Retrieval-Augmented Generation) agents that use real data

• Evaluate and debug agents using LangSmith

• Apply security best practices and deploy agents to production

• Finish with a capstone project: a complete, production-ready AI operations agent

Why this course?
Every section builds on the last — from your first LangChain agent, to graph-based workflows, to a fully evaluated and deployed system. You won't just learn concepts; you'll build a working agent at every stage, using patterns that hold up outside of a demo.

Who should take this course?
Python developers, software engineers, and AI/ML practitioners who want to move from "prompting an LLM" to building real, autonomous, production-grade agents.

By the end, you won't just know what an AI agent is — you'll have built and deployed one yourself.