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 themBuild agents with LangChain — chat models, structured outputs, and tool callingDesign reliable agent architecture with guardrails and tracingBuild stateful, multi-step workflows with LangGraphAdd memory and human-in-the-loop approval to your agentsBuild RAG (Retrieval-Augmented Generation) agents that use real dataEvaluate and debug agents using LangSmithApply security best practices and deploy agents to productionFinish with a capstone project: a complete, production-ready AI operations agentWhy 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.