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AI Agent Engineer

Name: AI Agent Engineer
Rating: 4.9 (16 reviews)

Build, evaluate, and deploy production-ready AI agents using LangChain, LangGraph, RAG, and LangSmith

Created byAnton Voroniuk• 1.250.000+ Students, Anton Voroniuk Support, George Paterakis

Last updated 8/2026

English

English [Auto],

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

Course content

11 sections • 42 lectures • 8h 46m total length

AI Agents Explained: What They Are & Why Everyone Is Building Them9:39
A clear, practical definition of an AI agent and why they've become the biggest shift in how we build with LLMs.
How AI Agents Work: A Complete Beginner's Guide10:48
The core loop behind every agent — reasoning, acting, and observing — explained from the ground up.
When to Use AI Agents (And When Not To)5:46
How to decide whether a task actually needs an agent, or whether a simpler LLM call will do the job better.
Why Most AI Agents Fail (And How to Fix Them)12:41
The most common reasons agent projects break in the real world, and how to design around them from day one.
What You Are Going to Learn in This Course4:40
A quick walkthrough of the full course roadmap, so you know exactly what you're building and why.
Introduction to AI Agent Engineering19:52
A written recap of the core agent concepts covered in this section, for quick reference as you move forward.

LangChain Chat Models Explained (System Prompts & Structured Output)22:44
How LangChain chat models work, and how to control their behavior with system prompts and structured output.
Build a LangChain Agent with Structured Output (Step-by-Step)13:54
A step-by-step build of a LangChain agent that returns clean, structured, predictable output.
Models, Messages, Prompts, and Structured Outputs11:05
A reference guide to LangChain's core message and prompt types, and how structured output ties them together.

LangChain Tools Explained: A Complete Beginner's Guide11:14
What tools are in LangChain, how agents decide to use them, and how to define your own from scratch.
Master LangChain Tool Calling & AI Agents19:52
A deeper look at how tool calling works under the hood, and how to make it reliable.
Build Your First LangChain Agent with Tools5:51
Build a complete LangChain agent equipped with real tools, from setup to execution.
Building Tools12:15
A practical reference on designing clear, reliable tools that agents can call correctly and consistently.

How to Design AI Agents That Actually Work14:40
Architectural principles that separate agents that work in a demo from agents that work in the real world.
Build Reliable LangChain Agents: Tools, Guardrails & Tracing14:33
Add guardrails and tracing to your agent so you can catch failures before they cause damage.
Code a Production-Ready LangChain Agent8:10
Bring architecture, guardrails, and tracing together into one production-ready LangChain agent.
Your First LangChain Agent12:28
A written recap tying together tools, reliability, and architecture into your first solid agent build.

LangGraph Fundamentals: Introduction to Agentic Workflows24:54
What LangGraph is, how it differs from a standard agent, and why graph-based workflows matter.
Build 9 LangGraph Agentic Workflows in 15 Minutes14:58
A fast, hands-on tour of nine different LangGraph workflow patterns you'll reuse throughout the course.
Build Your First Agentic Workflow with LangGraph11:51
Build your first complete, working agentic workflow in LangGraph from start to finish.
From Agents to Graph-Based Workflows9:55
A conceptual bridge explaining why and when to move from a single agent to a graph-based workflow.
Common LangGraph Patterns7:51
A reference list of the LangGraph patterns you'll reach for most often when designing workflows.

LangGraph AI Agents with Memory & Human-in-the-Loop25:25
How state, memory, and human-in-the-loop checkpoints work together inside a LangGraph agent.
Build a LangGraph AI Agent with Memory & Human-in-the-Loop11:45
Build a LangGraph agent that remembers past steps and pauses for human approval when needed.
Build LangGraph Agents with Human Approval & Memory9:33
A second, deeper build focused on approval workflows for higher-stakes agent actions.
Stateful Agents and Memory17:18
A reference guide to managing state and memory correctly across multi-step agent runs.
Human-in-the-Loop12:05
A practical guide to designing effective human-in-the-loop checkpoints without slowing agents down.

The Complete Guide to RAG for AI Agents23:00
What RAG is, how it works, and why it's essential for agents that need up-to-date, grounded knowledge.
How to Build Your First RAG AI Agent with LangChain9:47
Build your first RAG-powered agent in LangChain, from retrieval to generation.
Build Your First RAG AI Agent with LangGraph5:27
Rebuild the RAG agent as a LangGraph workflow to see how retrieval fits into a graph-based design.
Retrieval-Augmented Generation11:22
A reference guide to core RAG concepts: retrieval, chunking, embeddings, and grounding.

Introduction to Evaluating AI Agents with LangSmith15:15
Why evaluation matters for agents, and how LangSmith helps you measure agent quality.
How to Evaluate AI Agents with LangGraph & LangSmith9:36
Set up evaluation pipelines for LangGraph agents directly inside LangSmith.
Debugging and Evaluating with LangSmith8:57
A practical reference for tracing agent runs and debugging failures step by step in LangSmith.

How to Build Secure AI Agents for Production26:11
Key security risks in agent systems and how to defend against them before deployment.
How to Build Production-Ready AI Agents with LangChain17:58
What separates a prototype from a production system, and how to close that gap.
Production Agent Project6:42
Apply everything so far to build a complete, production-oriented agent project.
Security and Guardrails5:53
A guide to packaging your agent into a real, usable application beyond a script.
From Agent to Application7:36
A reference checklist of security practices and guardrails for agents handling real actions.

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 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.

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

AI Agent Engineer

What you'll learn

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Course content

AI Agent Foundations6 lectures • 1hr 3min

LangChain Essentials3 lectures • 48min

Tools and Your First Agent4 lectures • 49min

Reliable Agent Architecture4 lectures • 50min

LangGraph Fundamentals5 lectures • 1hr 9min

State, Memory, and Human Approval5 lectures • 1hr 16min

RAG Agents4 lectures • 50min

Evaluation with LangSmith3 lectures • 34min

Production and Security5 lectures • 1hr 4min

Capstone Project2 lectures • 17min

Requirements

Description

Who this course is for: