𝗪𝗵𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗮𝗶𝗹 | 𝗗𝗲𝗲𝗽 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 (𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 & 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲) скачать в хорошем качестве

𝗪𝗵𝘆 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗮𝗶𝗹 | 𝗗𝗲𝗲𝗽 𝗔𝗴𝗲𝗻𝘁𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱 (𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻 & 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲)

AI agents are becoming one of the most important building blocks of modern AI systems. But traditional 𝗥𝗲𝗔𝗰𝘁-𝘀𝘁𝘆𝗹𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝗼𝗳𝘁𝗲𝗻 𝗳𝗮𝗶𝗹 when tasks become complex. 𝗖𝗹𝗮𝘀𝘀 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 1. 𝗚𝗶𝘁𝗵𝘂𝗯: https://github.com/fnusatvik07/deep-a... 2. 𝗣𝗣𝗧 : https://drive.google.com/file/d/1-sPP... 𝗝𝗼𝗶𝗻 𝗼𝘂𝗿 𝗪𝗵𝗮𝘁𝘀𝗮𝗽𝗽 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: https://chat.whatsapp.com/JKa4RgYrBFN... 𝗝𝗼𝗶𝗻 𝗼𝘂𝗿 𝗥𝗔𝗚 𝗖𝗼𝘂𝗿𝘀𝗲: https://topmate.io/datasense/1930469 In this video we explore why AI agents fail and how Deep Agents solve these limitations. We will break down the Deep Agent architecture, understand the problems with current agent systems, and see how components like planning tools, file systems, and sub-agents enable more powerful autonomous systems. This concept is used in modern agent frameworks such as LangChain Deep Agents and Claude Agent SDK. 𝗪𝗵𝗮𝘁 𝗬𝗼𝘂 𝗪𝗶𝗹𝗹 𝗟𝗲𝗮𝗿𝗻 • What an AI Agent actually is • How ReAct agents work • Why traditional agents fail in complex workflows • Context window explosion problem • Context rot in long conversations • Why planning is necessary for agents • The importance of workspace and file systems • Sub-agents and task delegation • Deep Agent architecture explained 𝗗𝗲𝗲𝗽 𝗔𝗴𝗲𝗻𝘁 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 In this video we cover: • Planning Tool • Sub Agents • File System Workspace • System Prompt Control These components allow agents to: • break complex tasks into smaller subtasks • execute tasks in parallel • avoid context window bloat • manage long running workflows 𝗧𝗶𝗺𝗲𝗦𝘁𝗮𝗺𝗽 00:00 Introduction 02:10 What Are AI Agents? 06:30 ReAct Agents Explained (LLM + Tools + Memory) 11:45 How Modern Agent Systems Work 18:20 Limitations of Traditional Agents 25:40 Context Window Problem in Agents 34:10 Example: Moving Large Files with Agents 46:30 Why Context Window Gets Bloated 58:20 Workspace Concept for Agents 01:10:00 Token Efficiency in Agent Systems 01:22:30 Context Rot Explained 01:35:00 Why Summarization Doesn't Fully Solve Memory 01:47:30 How Humans Plan Complex Tasks 02:00:10 Planning vs Reactive Agents 02:12:20 Task Decomposition for Complex Problems 02:24:40 Sub-Agents and Parallel Execution 02:38:10 Deep Agents Architecture Explained 02:52:00 Key Takeaways & Future of Agentic AI