Building Real-World AI Agents

Building Real-World AI Agents

https://WebToolTip.com

Published 5/2026
Created by Alper Daldal
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Level: All Levels | Genre: eLearning | Language: English | Duration: 60 Lectures ( 4h 18m ) | Size: 1.32 GB

Explore AI agents and create your own using LangGraph or CrewAI.

What you'll learn
⚡ You will learn concepts such as large language models, generative AI, and prompts, which are essential for understanding the AI Agent architecture.
⚡ You will discover what AI Agents are and how they differ from Generative AI and Agentic AI.
⚡ You will study the fundamental components that make up an AI Agent, such as personality, memory, tools, reasoning, and planning.
⚡ You will discover powerful agentic patterns like ReAct, CoT, ReWOO, ToT, and reflection, and use them to design and build AI Agents.
⚡ You will study multi-agent architectures, analyze examples such as Supervisor and Swarm, and build your own multi-agent systems using these patterns.
⚡ You will discover how to bring humans into the AI Agent loop and solidify your learning with real code examples.
⚡ You will explore Agentic RAG and learn how to use it in your own AI Agents through practical code examples.
⚡ You will discover how AI Agents communicate through protocols like MCP, A2A, and ACP.
⚡ You will explore widely used AI Agent frameworks such as LangGraph and its core foundation, LangChain.
⚡ By learning concepts such as states, nodes, and edges in LangGraph, you will be able to build your own workflows.
⚡ You will learn different ways to use memory in LangGraph through practical code examples.
⚡ You will learn how to use both built-in and custom tools within the LangGraph framework.
⚡ You will explore examples of agent collaboration using LangGraph and learn to build similar systems yourself.
⚡ You will discover CrewAI, a powerful framework for developing AI Agents.
⚡ You will explore the key building blocks of CrewAI—teams, tasks, and agents—and learn how to use them in your own projects.
⚡ You will study the YAML-based configuration used to define tasks and agents in CrewAI and learn to create your own YAML files.
⚡ You will discover how to design and run flows in CrewAI while collaborating with a crew.
⚡ You will be able to develop AI Agents in CrewAI that can write and execute code.
⚡ You will learn how to improve your AI Agent’s performance using CrewAI’s testing and training features.

Requirements
❗ Possession of basic programming and algorithm development or comprehension skills
❗ While Python knowledge is not required, it may assist in understanding the provided code examples