AI Agent Engineer

Build, evaluate, and deploy production-ready AI agents using LangChain, LangGraph, RAG, and LangSmith
What you'll learn

---

Build AI agents from scratch using LangChain and LangGraph
Design agents with tools, structured outputs, and tool-calling
Build stateful, multi-step agentic workflows with memory and human-in-the-loop approval
Implement Retrieval-Augmented Generation (RAG) to ground agents in real data
Evaluate, debug, and trace agent behavior using LangSmith
Apply guardrails and security best practices for production AI agents
Deploy AI agents from local development to a live production environment
Complete a capstone project: a full production-ready AI operations agent
Requirements

---

Basic Python programming knowledge (variables, functions, loops)
No prior experience with AI agents, LangChain, or LangGraph required
Familiarity with calling APIs is helpful, but not mandatory
A computer with internet access to write and run code
Description

---

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 actuallydo 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 acapstone 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.
Who this course is for

---

Python developers who want to move from prompting LLMs to building autonomous agents
Software engineers looking to add LangChain and LangGraph to their skill set
AI/ML practitioners who want to build reliable, production-grade agent systems
Developers preparing for AI/agent engineering roles
Anyone who wants to go from a simple chatbot to a secure, evaluated, deployed AI agent
Homepage

---