Agentic Ai Internals: Build An Agent From Scratch
Published 3/2026
Created by Eden Marco
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 57 Lectures ( 5h 37m ) | Size: 5 GB
What you'll learn
✓ Become proficient in LangChain
✓ Have end to end working LangChain based generative AI agents
✓ Prompt Engineering Theory: Chain of Thought, ReAct, Few Shot prompting and understand how LangChain is build under the hood
✓ Context Engineering
✓ Understand how to navigate inside the LangChain opensource codebase
✓ Large Language Models theory for software engineers
✓ LangChain: Lots of chains Chains, Agents, DocumentLoader, TextSplitter, OutputParser, Memory
✓ RAG, Vectorestores/ Vector Databases (Pinecone, FAISS)
✓ Model Context Protocol (MCP)
✓ LangGraph
Requirements
● This is not a beginner course. Basic software engineering concepts are needed
● I assume students will be familiar software engineering subjects such as: git, python, pipenv, environment variables, classes, testing and debugging
● No Machine Learning experience is needed.
Description
This course contains the use of artificial intelligence
Please note that this is not a course for beginners. This course assumes that you have a background in software
engineering and are proficient in Python. I will be using Pycharm IDE but you can use any editor you'd like
since we only use basic feature of the IDE like debugging and running scripts .
Who this is for: Software developers, data scientists, and AI/ML engineers proficient in Python. This is not a beginner course.
Welcome to AI Agents with LangChain. This course teaches you how AI agents actually work - then you build them from scratch.
You'll go deep into agent internals: how LLMs make decisions, how function calling works, how prompts drive
agent behavior, and how to build agents with LangChain.
What you'll learn
• LLM and GenAI foundations
• Prompt engineering, Context engineering
• Tool calling and function calling
• Agent tracing with LangSmith
• Deep agents with LangGraph
• Open source models
• Output parsers and structured output
Everything is hands-on - real code, real projects. Uses PyCharm but any Python IDE works.
DISCLAIMERS
• Please note that this is not a course for beginners. This course assumes that you have a background in software engineering and are proficient in Python.
I will be using Pycharm IDE but you can use any editor you'd like since we only use basic feature of the IDE like debugging and running scripts.
Who this course is for
■ Software Engineers that want to learn how to build Generative AI based applications with LangChain and LangGraph
■ Developers that want to learn how to build Generative AI based applications with LangChain and LangGraph
■ Engineers that want to learn how to build Generative AI based applications with LangChain and LangGraph
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