AI Agent EngineerPublished 8/2026
Created by Anton Voroniuk, Anton Voroniuk Support, George Paterakis
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels |
Genre: eLearning |
Language: English |
Duration: 42 Lectures ( 8h 46m ) |
Size: 3.7 GB
⚡ 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
DescriptionThis 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
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