AI can feel overwhelming, but this hands-on course makes it practical, approachable, and fun. You will start with AI fundamentals and model mechanics like transformers and tokens, before moving into the architecture of tools, workflows, and true agents. You will build and experiment with four distinct agents, seeing firsthand how single agents evolve into a multi-agent system. Finally, you will learn how OpenClaw woks to gain the confidence needed to design, build, test, and deploy real-world AI agents.

Course resources: https://kode.wiki/3SrN8GO

Course created by @KodeKloud

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

Chapters
Section 1 & 2: LLM & Core Concepts
- 00:00:00 Course Welcome & Architecture Fundamentals
- 00:07:54 Hallucinations, Model Evolution, & Competitors
- 00:15:23 Developer Fundamentals & Working with APIs
- 00:17:54 Token Economics & Managing Context Windows
- 00:31:13 API Message Roles & Prompt Engineering Nuances

Section 3 & 4: Code Setup & Tool Architecture
- 00:38:29 Hands-On Lab: Practice Labs & First API Call
- 00:45:01 Multi-Turn History, API Costs, & Trade-offs
- 00:51:08 Architecture Overview: Tool Schemas & Execution Loops
- 00:58:20 Predefined AI Workflows vs. Autonomous Agents

Section 5: Building Agents from Scratch
- 01:08:16 The Core Loop: Perceive, Reason, Act
- 01:13:15 Hands-On Lab: Engineering the Standard `While` Loop
- 01:21:13 Planning Limits, Brittle Logic, & Mitigation Guardrails
- 01:26:19 Memory Managers: Sliding Windows & Vector DBs
- 01:33:56 Multi-Agent Frameworks & Inter-Agent Communication
- 01:43:08 Deconstructing Production Failures & Error Handling
- 01:52:20 Grounding Data & Prepackaged Frameworks (LangChain, Mastra)
- 02:00:25 Hands-On Lab: Building a Production Personal Assistant

Section 6: Deep Case Study (OpenClaw)
- 02:09:25 OpenClaw Codebase Tour & File Architecture
- 02:14:39 Custom Layouts, Terminal Interfaces, & Concurrency Locks
- 02:30:44 Testing Non-Deterministic Agents & LLM-as-a-Judge Evals
- 02:39:30 Observability, Tracking Performance Metrics, & Optimization Levers
- 02:51:55 Agent Security, Advanced Prompt Assemblies, & Wrap-up

🎉 Thanks to our Champion and Sponsor supporters:
👾 @omerhattapoglu1158
👾 @goddardtan
👾 @akihayashi6629
👾 @kikilogsin
👾 @anthonycampbell2148
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👾 @CloudVirtualizationEnthusiast
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👾 @martinmacchia1564
👾 @ulisesmoralez4160
👾 @_Oscar_
👾 @jedi-or-sith2728
👾 @justinhual1290

--

Learn to code for free and get a developer job: https://www.freecodecamp.org

Read hundreds of articles on programming: https://freecodecamp.org/news

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This Agentic AI Full Course 2026 by Simplilearn is designed to help beginners understand how modern AI systems can plan tasks, make decisions, and automate workflows. The course starts with the basics of Agentic AI, how it works, and how it is different from traditional AI models. You will then learn about AI agents, agent workflows, tools, and real-world use cases in simple terms. The course also introduces concepts like prompt engineering, automation, and building AI-powered applications. As you progress, you will explore practical examples and future trends of autonomous AI systems. This course is ideal for anyone who wants to understand next-generation AI technology and how it can be used in real business and development scenarios.

Following are the topics covered in the Agentic AI Full Course 2026:

00:00:00 - Introduction To Agentic AI
00:03:11 - Chapter 1: Basics of Agentic AI
00:08:06 - Chapter 2: Core Concepts of Agentic AI
00:15:15 - Chapter 3: Agentic AI Frameworks & Libraries
00:22:09 - Chapter 4: Memory and Long-Term Context
00:30:01 - Chapter 5: Planning & Decision Chains
00:36:25 - Chapter 6: Advanced Use-Cases of Agentic AI
00:39:48 - Chapter 7: Agent Evaluation and Prompt Optimization
00:43:50 - Chapter 8: Risks, Ethics, and Future of Agentic AI
00:47:27 - Chapter 9: Hands-On Project

✅Subscribe to our Channel to learn more about the top Technologies: https://bit.ly/2VT4WtH

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➡️ About Applied Generative AI Specialization

The Applied Gen AI Course equips learners with the skills to build AI-driven solutions. Learn to design, train, and deploy models while addressing ethics, bias, and compliance. Gain industry-relevant experience through hands-on projects to drive innovation and stay competitive in an AI-powered world.

Key Features

✅ Get a program completion certificate from Michigan Engineering Professional Education
✅ Engage in 70+ hours of live online sessions led by industry experts
✅ Access a comprehensive curriculum covering Python, AI literacy, and GenAI principles (VAEs, GANs, and LLMs)
✅ Explore agentic frameworks and create adaptive AI agents using LangChain
✅ Implement advanced prompt engineering, RAG, and fine-tune models for domain-specific AI solutions
✅ Apply best practices for AI transparency, fairness, security, and regulatory compliance
✅ Work with 12+ modern AI tools, including OpenAI, Stable Diffusion, Microsoft Copilot, and Streamlit
✅ Earn a course completion certificate hosted on the Microsoft Learn portal for Microsoft courses
✅ Experience flexible learning with session recordings
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Skills Covered
✅ Prompt Engineering
✅ Agentic Frameworks
✅ AI Agents
✅ LangChain for Workflow Design
✅ Retrieval Augmented Generation RAG
✅ LLM Fine Tuning
✅ Stable Diffusion
✅ Variational Autoencoders VAEs
✅ Generative Adversarial Networks GANs
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✅ Transformers
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Want to build AI agents and turn them into a real income source? In this complete beginner's guide, you'll learn how to build, package, and sell AI agents—even if you're starting from zero.

AI agents are becoming one of the biggest opportunities in AI, and businesses are actively looking for ways to automate customer support, lead generation, appointment booking, sales, and other repetitive tasks.

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🔥 In this video, you'll learn

. What AI agents are and how they work
. How to build your first AI agent from scratch
. Which AI tools you can use to create agents
. How to find businesses that need AI agents
. How to choose profitable AI agent services
. How to price and sell your AI agents
. How to get your first AI agent client
. How to turn AI agents into a scalable business
. Common mistakes beginners should avoid

Download - https://www.theiscale.com/coho....rt/course-details/ai

Timestamp
00:00:00 - Introduction & The Rise of AI Agents
00:01:17 - What is an AI Agent?
00:02:28 - Key Course Outcomes & Portfolio Projects
00:03:44 - AI Assistant vs. AI Agent
00:07:40 - Core Components of an AI Agent
00:10:15 - AI for Everyone Cohort Program Overview
00:12:27 – Practical Project - Agent 1: Voice AI Agent for Customer Support (ElevenLabs)
00:16:55 - Voice Agent Configuration & Live Testing
00:20:48 – Practical Project -Agent 2: Business Support & Invoice Automation Agent (Zapier)
00:26:03 - Zapier Workflow Setup, Tool Integrations & Live Testing
00:34:53 - Monetization Strategy: Packaging & Scaling AI Agents
00:36:55 - 7 Proven Business Models for Selling AI Agents
00:46:38 – Practical Project - Agent 3: Startup Branding & Consultant AI Agent (n8n)
00:51:05 - Google AI Studio API Key Setup & Testing n8n Agent
00:56:14 - Practical Project - Agent 4: E-Commerce Support & Order Tracker Agent (Langflow)
00:59:03 - Installing & Initializing Langflow
01:01:24 - Building the Langflow Architecture (LLM, Knowledge Base & Memory)
01:07:28 - Playground Live Testing & Final Conclusions

Whether you're a student, freelancer, developer, creator, or complete beginner, this guide will give you a practical roadmap to start building and selling AI agents

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Learn how to build and sell AI agents in 2026 with this complete free course — no coding experience required. In this step-by-step tutorial, you'll master n8n automation, build four real-world AI agents (Telegram expense tracker, website lead generation chatbot, Retell AI voice agent for speed-to-lead qualification, and a full-stack sales copilot with a Lovable frontend), and learn how to monetize your AI agent skills by selling to businesses. I cover everything from AI agent foundations, LLMs, prompt engineering, API integrations, Google Solar API, Airtable CRM, vector store knowledge bases, webhook architecture, and vibe coding — plus a proven blueprint for launching your AI automation agency as either a builder or consultant.

0:00:00 Intro
0:03:53 AI Agent Foundations
0:30:10 Build 1: Telegram Receipt Analysis Assistant
0:56:23 Self Host Your n8n
1:00:41 Build 2: Website Lead Gen Chatbot
1:40:37 Build 3: Speed to Lead Voice Agent
2:19:25 Build 4: Sales Rep Copilot
2:59:36 Monetize Your AI Agents

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Instantly AI: https://refer.instantly.ai/305ykhekal4k
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Claude Code: https://code.claude.com/docs/en/quickstart
Visual Studio Code: https://code.visualstudio.com/
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Instantly MCP:
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Prompt Text Agent:
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Voice Agent 1 (Reactivation):
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Voice Agent 2 (Speed to Lead):
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Summary ⤵️
This is the FREE 8-hour course on how to build and sell AI Agents in 2026. And don't worry, you don't have to be technical to start.

After watching this video, you will be able to run an entire business.

I walk you through how to build production grade voice agents.
How to outreach to clients, I show you a real live sales call, and how you deliver to clients with fully custom AI Agent systems.

We take it slow, so you will understand it, even if you are non technical.

We also go over the most important part, which is "How do you actually get clients". I show you how to build both an outbound and inbound client getting system by using a mix of Cold email + Upwork, to destroy the competition.

This is the only video you need, if you want to build an AI business in 2026. Hope you enjoy the tutorial!

⭐️ Start Free Community with 150,000 AI entrepreneurs ⤵️
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Follow Me On Socials ⤵️
https://www.instagram.com/ai_with_albert/
https://www.youtube.com/@albertolgaard

Chapters
00:00:00 Introduction
00:05:26 Finding and Closing 1st Client
00:46:20 Building and Selling the Demo
01:42:00 Live Sales Call
03:23:43 Cold Outreach
03:58:24 Full Technical Build
05:57:00 Mistakes, Niches and Pricing
06:27:06 Deployment, Going Live and Scaling

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Learn the future of AI with this complete Agentic AI course by Intellipaat. In this video, you’ll understand the difference between Generative AI and Agentic AI, explore AI Agents, LangChain, Embeddings, Large Language Models (LLMs), RAG Agents, and Model Context Protocol (MCP) with detailed explanations and practical insights.

This Agentic AI Full Course for Beginners 2026 is designed for students, developers, and professionals who want to build a strong foundation in autonomous AI systems and modern AI workflows. From Agentic AI design patterns to frameworks and real-world AI concepts, this course covers everything step-by-step for beginners as well as intermediate learners.

Topics Covered: Introduction to Agentic AI, Generative AI vs Agentic AI, Generative AI Systems, LangChain, AI Agents, Agentic AI Design Patterns, Agentic Frameworks, Embeddings, Large Language Models, RAG Agents, and MCP. Start learning with Agentic AI Full Course for Beginners 2026 and stay ahead in the AI revolution.

📖 Below are the concepts covered in the video on "Agentic AI Full Course":
00:00:00 – Introduction to Agentic AI Course
00:01:26 – Generative AI vs Agentic AI
00:11:05 – What is a Generative AI System?
01:03:56 – What is LangChain
01:23:40 – AI Agents
01:41:58 – Agentic AI Design Patterns
02:33:45 – Agentic Frameworks & Basics
04:34:33 – Embeddings
05:06:01 – Large Language Model
06:54:12 – RAG Agent
07:52:14 – Model Context Protocol

#AgenticAI #AIAgents #LangChain #RAG #LLM #ArtificialIntelligence #GenerativeAI #Intellipaat

➡️ About the Course
The Agentic AI Systems & Design course by Intellipaat is an advanced program focused on building autonomous AI agents that can plan, reason, and execute tasks using tools like APIs, coding environments, and web search. Unlike traditional Generative AI courses, this program goes beyond prompt-based outputs and teaches you how to design production-grade AI systems capable of independent decision-making and complex problem-solving. It is ideal for professionals such as data scientists, AI/ML engineers, and developers who want to move from basic AI usage to creating intelligent, real-world applications powered by agentic workflows

📌 Do subscribe to Intellipaat channel & come across more relevant Tech content: https://goo.gl/hhsGWb

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📚For more information, please write back to us at [email protected] or call us at IND: +91-7022374614 / US : 1-800-216-8930

A complete crash course on agentic ai using langchain. This is also a course on generative AI. In this video, we will discuss theoratical foundations of generative AI and agentic AI. Langchain will be used for hands on programming. We will then build two projects (1) shopping agent (2) telecom RAG chatbot using langchain, chromadb, llama vision model, Qwen, gemini LLMs, streamlit, sqlite, huggingface and sentence transformers.

Download the resources here: https://codebasics.io/resource....s/agentic-ai-crash-c

No code agents: https://codebasics.io/ - Check "Our Live Cohorts" section

Langgraph tutorial: https://youtu.be/CnXdddeZ4tQ
Customer care support: https://youtu.be/Jekzc6BM5_w
Workflow automation: https://youtu.be/QBI5bmbgL38

Check out OpenRouter: https://openrouter.plug.dev/6QpCcgh

Codebasics AI Pro keeps you ahead of it: weekly tool tests, live build sessions, and AI you can actually apply at work: https://codebasics.io/ai-learning-for-professionals

⭐️ Timestamps ⭐️

0:00:00 Intro

0:01:53 What is Gen AI

0:03:10 Traditional AI

0:05:41 Transformer Architecture

0:11:13 LLM Parameters

0:13:28 LLM Analogy Based Explanation

0:18:41 API Key Setup

0:21:01 Simple LLM call using LangChain

0:41:50 RAG Theory

0:47:52 Vector DB - Hands On

0:53:50 Rag - Hands On

1:02:54 AI Agent Intro

1:03:31 What is an AI Agent

1:10:18 ReAct Loop

1:10:49 AI Agent Hands on

1:21:28 Multi-Agent system

1:21:48 Multi-Model Agent theory

1:23:31 Multi-modal agent – hands on

1:29:28 Evaluation

1:34:07 Guardrails

1:36:41 Project 1 – Shopping Agent

2:06:57 Project 2 – Telecom RAG

2:22:46 No Code Agents

Do you want to learn technology from me? Check https://codebasics.io/?utm_source=description&utm_medium=yt&utm_campaign=description&utm_id=description for my affordable video courses.

Need help building software or data analytics/AI solutions? My company https://www.atliq.com/ can help. Click on the Contact button on that website.

🎥 Codebasics Hindi channel: https://www.youtube.com/channe....l/UCTmFBhuhMibVoSfYo

#️⃣ Social Media #️⃣

🧑‍🤝‍🧑 Discord for Community Support: https://discord.gg/r42Kbuk
📸 Codebasics' Instagram: https://www.instagram.com/codebasicshub/
📝 Codebasics' Linkedin : https://www.linkedin.com/company/codebasics/
📱 Dhaval's X handle : https://x.com/dpcodebasics

------

📝 Dhaval's Linkedin : https://www.linkedin.com/in/dhavalsays/
📝 Hem's Linkedin: https://www.linkedin.com/in/hemvad/

📽️ Hem's Instagram for daily tips: https://www.instagram.com/hemvadivel/
📸 Dhaval's Personal Instagram: https://www.instagram.com/dhavalcodebasics

🔗 Patreon: https://www.patreon.com/codeba....sics?fan_landing=tru

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Most people use Claude wrong — they paste a task into chat and call it an agent. A real agent has a process, knows when it's uncertain, and asks before it acts. In this tutorial I show you how to build one with no code, no APIs, no developer setup — just Claude Desktop and a folder on your computer.

We cover the 3 levels of working with Claude, how to set up your Claude Code workspace, why the CLAUDE.md file is the most important piece of the whole system, and how planning mode stops Claude from guessing. Then we build two real agents live. Plus the 5 mistakes that kill most agent builds.

Chapters:
00:20 What an AI Agent Actually Is
03:05 The Three Things That Make an Agent Real
04:32 Setting Up Your Workspace
05:38 CLAUDE.md — The File That Changes Everything
08:11 The Habit That Makes or Breaks Results
10:13 How Agent Systems Are Actually Structured
12:04 Building Your First Real Workflow
13:51 Running the Agent on a Live Task
15:22 Iterating on the Output
16:26 Building a Second Workflow
17:53 The Five Mistakes That Kill Agent Workflows
20:12 Expanding Your Agent Stack Over Tim

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Welcome to the most comprehensive free course on AI automation and AI agents for beginners using n8n.

In this 8+ hour deep-dive, I’ll walk you step-by-step through what you need to go from complete beginner to building and selling real AI-powered automations, no coding required.

We’ll cover what AI agents are, why they’re a smart investment right now, and how to use n8n to build powerful workflows with a free two-week trial.

You’ll learn n8n foundations, how to set up APIs and HTTP requests, and how to use tools like agent memory, multi-agent systems, and prompting to create scalable systems.

We’ll also explore essential concepts like webhooks, MCP servers, and self-hosting, plus a ton of advanced strategies packed into easy-to-follow lessons.

By the end of this course, you’ll have built over 15 working AI automations that you can use or sell. This video is insanely value-packed, so make sure to save it and come back to it whenever you hit a roadblock.

If you’re serious about mastering AI agents and automation, this course is the only one you’ll need.

Sponsorship Inquiries:
📧 [email protected]

Connect with me:
https://www.linkedin.com/in/nateherkelman/
https://x.com/nateherk
https://www.instagram.com/nateherk/

WATCH NEXT:
https://youtu.be/QhujcQk8pyU?si=kpGWo0cF4aiAaT68

TIMESTAMPS
00:00 Course Agenda (8+ Hours)
01:43 AI Agents Simplified
09:02 The Industry Opportunity
12:05 n8n Foundations (workflows, variables, JSON)
40:45 n8n Data Types
48:25 23 Workflow Templates FREE
49:09 Step by Step AI Workflows
51:25 1) RAG Pipeline & Chatbot
01:11:02 2) Customer Support Workflow
01:22:06 3) LinkedIn Content Creator Workflow
01:32:58 Bonus Workflow: Invoice Processing
02:09:54 APIs Explained Simply
02:34:21 Perplexity API Setup
02:40:36 Firecrawl API Setup
02:54:48 Apify API Setup
03:02:50 Image Generation API Setup
03:21:11 Video Generation API Setup
03:47:15 Don't Use AI Agents Wrong
04:00:26 Build Your First Agent!
04:24:06 Postgres & Supabase Setup
04:34:46 Multi Agent System Architecture
04:55:22 4 Agent Architectures
05:12:52 Prompting AI Agents
05:39:58 Output Parsing
05:46:25 Human in the Loop
06:02:16 Error Workflows
06:11:47 Dynamic Brain
06:25:03 Webhooks
06:31:24 Voice Email Agent
06:47:31 Front End w/ Lovable
07:06:59 What is MCP?
07:19:49 MCP & Self Host n8n Setup
07:45:05 Lessons from 6 Months of Building Agents
08:25:36 If You Made it Here, You Rock


Gear I Used:
Camera: Razer Kiyo Pro
Microphone: Blue Yeti USB

Ready to become a certified watsonx Generative AI Engineer? Register now and use code IBMTechYT20 for 20% off of your exam → https://ibm.biz/BdpPUq

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We're all using AI agents, but they still lack the procedural knowledge real work needs. Martin Keen explains how agent skills, LLMs, RAG, and MCP help agents follow workflows, automate tasks, and make smarter decisions. Learn how to build better AI agents. 🤖

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/BdpPUy

#aiagents #agentskills #llm #mcp

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━━━━━━━━━━━━━━━━━━━━━━

Millions of AI agents are already working right now — and almost nobody outside
of tech knows how to build one. In this video you'll build 3 no code AI agents
in 15 minutes, including one that builds and manages an entire army of AI agents
for you. Zero coding. Nothing to install. Total cost: under $10.

Everyone learned ChatGPT and Claude. Almost nobody learned what came after it.

Here's the difference. Today you open a tab, ask a question, get an answer, close
it and tomorrow you start from zero. An AI agent doesn't work like that. You
build it once, it remembers you, and it keeps working long after you've shut the
laptop.

This is a complete no code AI agent tutorial for beginners. No developer
background needed, no terminal, no API keys pasted from a doc you don't
understand. Everything runs in your browser, on whatever laptop you already own.

WHAT YOU'RE BUILDING:

Level 1 — Your first AI agent. The simplest working version there is.
Level 2 — An AI agent that builds and manages an army of other AI agents,
running multiple jobs at the same time.
Level 3 — A fully autonomous agent that runs on its own every morning and has
a report waiting for you before you wake up.

We're using Hyperagent, built by the founder of Airtable. A normal AI forgets
everything about you the second you close the tab. This one remembers your whole
setup and keeps working on its own — less like a chatbot you re-explain yourself
to every time, more like an employee you hire once.

People are already selling agent builds like these for a couple thousand dollars.
This one cost under ten dollars to make. That's what a one person business looks
like in 2026.

━━━━━━━━━━━━━━━━━━━━━━

⏱ TIMESTAMPS
0:00 Everyone learned ChatGPT. Nobody learned this
0:40 The real question: how AI makes money
1:04 The tool that makes this possible
1:21 Free credits + all prompts
1:38 BUILD 1: The offer that brings money in
2:29 Building the wedding decor agent
3:38 One photo, three decorated venues
4:12 Saving it as a reusable agent
4:44 The pricing page that sells itself
5:16 What it actually cost: $11
5:39 BUILD 2: The agent that builds a whole company
6:53 Setting up the startup team
7:37 How it saves money mid-task
7:54 The research report
8:19 The builder and the brand
9:03 Finding 7 real customers, live
9:22 Turning it into reusable skills
9:42 Plugging into Gmail, Slack, GitHub
9:58 BUILD 3: The agent that runs it all
10:20 Setting up your always-on analyst
11:52 The agent that grades itself
12:12 Your daily report, built automatically
12:45 The whole one person business
13:01 Claim your credits

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❓ QUESTIONS PEOPLE ASK

Do I need to know coding to build an AI agent?
No. Every build in this video is no code. Nothing gets installed and nothing runs
locally.

What is an AI agent, in simple terms?
A normal AI answers one question and forgets you. An agent is set up once, keeps
your context, and keeps working on its own without being prompted again.

Is Hyperagent free?
No, it's a paid tool. The first 500 people through the link get $500 in credits,
which covers everything built in this video.

Can I sell AI agents I build?
People are charging a couple thousand dollars for builds like these. The video
shows exactly what goes into one.

━━━━━━━━━━━━━━━━━━━━━━

#AIAgents #NoCodeAI #AIAutomation #AITutorial #Hyperagent

--------

To Know More,
Follow Vaibhav Sisinty On ⤵︎

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https://www.linkedin.com/in/vaibhavsisinty

Find the full "AI Agents for Beginners" Course and code samples here ➡️ https://aka.ms/ai-agents-beginners

In this lesson:
00:00 Introduction
00:00 Lesson 1 What are AI agents?
5:55 Lesson 2 Which agent framework to use
14:14 Lesson 3 How to design good AI agents
19:35 Lesson 4 What is the Agent Tool Use Design Pattern?
25:47 Lesson 5 What is agentic RAG?
33:03 Lesson 6 How to build effective AI agents
38:54 Lesson 7: What is the AI Agent Planning Design Pattern?
44:20 Lesson 8 How to use a multi-AI agent system
51:22 Lesson 9 How can AI agents improve?
58:32 Lesson 10 How to deploy AI agents into production

I sit down with Remy Gaskell to break down how anyone can build AI agents to run entire departments of their business. Remy walks through the core concepts: agent loops, context files, memory, MCP tool connections, and skills. We put everything together by building a fully functional executive assistant live on screen. This is a beginner-friendly crash course that covers Claude Code, Codex, Cowork, Antigravity, Manus, and OpenClaw, showing that once you understand how to "drive," you can jump into any agent platform. By the end, listeners know exactly how to set up markdown-based context files, connect their everyday tools, and create reusable skills that compound over weeks and months.

Timestamps
00:00 – Intro
01:35 – Agents vs Chat
03:22 – The Agent Loop
05:46 – How Agents work
06:39 – Demoing Agents (Claude Code, Codex, Antigravity)
08:52 – Security and Agent Permissions
10:43 – Comparing Results Across Three Platforms
13:57 – Startup Idea: Cold Email Website Offer
14:50 – Folder Structure and Department-Based Agents
15:52 – Onboarding an Agent Like a Real Employee
17:05 – Voice-to-Text With Monologue and WhisperFlow
18:04 – Chat Memory vs. Agent Memory
19:34 – Building the agents md
22:20 – Context Engineering Over Prompt Engineering
24:29 – How Memory Compounds and Reduces Errors
30:27 – How Big Can memory md Get?
31:43 – Connecting Tools via MCP (Model Context Protocol)
34:49 – Working in Claude Code for High-Value Tasks
37:09 – Why the Real Value Is in Stacking, Not Summarizing
40:04 – What Are Skills? (SOPs for AI)
43:08 – Creating Skills
48:36 – Real-World Example: Ads Analyst Skill: 4-Hour Process in Minutes
50:37 – Chaining Skills together
52:01 – Real-World Example: Automated Car Search
53:34 – OpenClaw and Migrating Agents to More Autonomous Platforms
55:19 – Which Platform Should Beginners Start With?
56:28 – Global vs. Project-Level Skills, Context, and MCPs

Key Points

* Agent platforms (Claude Code, Codex, Cowork, Antigravity, Manus, OpenClaw) are all running the same observe-think-act loop under the hood — learning one means you can use any of them.
* The shift from chat to agents requires moving from prompt engineering to context engineering: load the agent with rich context so simple prompts produce excellent results.
* A memory md file creates a self-improving loop where the agent learns preferences across sessions and makes fewer errors over time.
* MCP (Model Context Protocol), built by Anthropic, acts as a universal translator between your agent and every tool it needs — Gmail, Calendar, Stripe, Notion, and more.
* Skills are reusable SOPs packaged as markdown files; once you explain a process once, you can invoke it repeatedly, and they compound as you add three to five per week.
* Scheduled tasks turn skills into automated workflows — morning briefs, car searches, ad library analyses — that run on a cron without any manual trigger.

Numbered Section Summaries

1. The Agent Loop in Action

Remy kicks off with a live demo, sending the same prompt — "build a minimalist portfolio site for Greg Isenberg" — to Claude Code, Codex, and Antigravity simultaneously. All three platforms run the same observe-think-act loop: research the subject, write the code, spin up a preview, and verify the result with a screenshot. The demo makes it tangible that every agent harness is just a different car with the same engine.

2. Onboarding Your Agent Like a Real Employee

Remy shows that without context, an agent asked to "write me a cold email" has no idea who you are or what you sell. The fix is an agents.md (or Claude.md) file — a persistent context document loaded at the start of every session. You fill it with your role, business details, tools, and working preferences, and the result is that a two-word prompt produces a fully informed output.

3. Memory That Compounds

Chat models store memory invisibly in the cloud; agents require you to build it intentionally. Remy adds a memory.md file and a simple instruction in the context file: "When I correct you or you learn something new, update memory.md." Preferences like tone, email sign-offs, and design choices persist across sessions, and errors decrease over time.


The #1 tool to find startup ideas/trends - https://www.ideabrowser.com/

LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/

The Vibe Marketer - Resources for people into vibe marketing/marketing with AI: https://www.thevibemarketer.com/

FIND ME ON SOCIAL
X/Twitter: https://twitter.com/gregisenberg
Instagram: https://instagram.com/gregisenberg/
LinkedIn: https://www.linkedin.com/in/gisenberg/

FIND REMY ON SOCIAL
X:https://x.com/remy_gaskell
Youtube: https://www.youtube.com/@aiwithremy
Instagram: https://www.instagram.com/aiwithremy/

Learn to build and deploy intelligent AI agents that bridge the gap between LLMs and real-world automation to handle complex research and data analysis. This comprehensive course takes you from zero to deployment, using OpenAI and Node.js to create a production-ready tool complete with a PostgreSQL database on Render.

🔗 Sign up for Render: https://www.render.com

Course developed by @aniakubow

💻 Code https://github.com/kubowania/slack-ai-agent

🏗️ Render provided a grant to make this course possible.

⭐️ Chapters ⭐️
0:00:00 Introduction
0:02:24 Getting started on Render.com
0:04:14 Setting up our project
0:08:19 Packages that we need
0:11:26 Slack AI Agent logic
0:14:53 Setting up Slack events
0:29:48 Analysing members logic
0:57:06 Posting analysis to our Slack channel
1:09:06 Start logic
1:16:00 Secret variables and where to find them
1:22:54 Database logic
1:34:34 Testing our app works
1:38:15 Deploying our app

Learn how to build production-ready multi-agent systems and automate workflows using LangChain and LangGraph. You'll master everything from core agentic fundamentals and Pydantic validation to advanced sequential, parallel, and conditional LangGraph workflows. Along the way, you'll implement chat memory, RAG, and human-in-the-loop controls, and finish by deploying your applications to AWS and Render through real-world projects like a custom ChatGPT, trip planner, and auto content agent.

Code: https://github.com/entbappy/Co....mplete-Agentic-AI-Co

Bappy's YouTube Channel: https://www.youtube.com/@dswithbappy
Bappy's LinkedIn profile: https://www.linkedin.com/in/boktiarahmed73/
Bappy's Udemy: https://www.udemy.com/user/boktiar-ahmed-bappy/

❤️ Support for this channel comes from our friends at Scrimba – the coding platform that's reinvented interactive learning: https://scrimba.com/freecodecamp

⭐️ Chapters ⭐️
- 00:00:00 Introduction & Planning
- 00:08:33 Evolution from LLMs to Agentic AI
- 00:41:26 Agentic AI: Core Characteristics & Components
- 01:33:18 Asynchronous Programming for AI Agents
- 02:06:23 Pydantic for AI Agents - Pydantic Data Validation
- 03:27:08 Building End-to-End Single AI Agent System using LangChain
- 04:49:37 Building End-to-End Multi-Agent AI System with LangChain
- 06:30:27 What is LangGraph & Why It’s Required? | LangChain vs LangGraph
- 07:44:43 Understanding LangGraph Core Components
- 08:29:07 Build Sequential Workflows in LangGraph
- 09:19:04 Implement Parallel Workflows in LangGraph
- 10:15:27 Master Conditional Workflows in LangGraph
- 11:06:05 LangGraph Iterative Workflows Explained
- 11:35:51 Build Your First Agentic Chatbot with LangGraph
- 12:43:49 Why Persistence is Required in LangGraph
- 13:51:00 Streaming Responses in Agentic Chatbot with LangGraph
- 14:10:02 How to Add Chat Threading in Agentic Chatbot using LangGraph
- 14:42:02 Build Permanent Chat Persistence Memory with LangGraph and Database
- 15:16:31 Monitor Your Agentic Chatbot with LangSmith & LangGraph
- 15:40:24 How to Integrate Tools in Agentic Chatbot with LangGraph
- 16:54:17 Implement RAG in Agentic Chatbot with LangGraph
- 17:50:27 Implement Human-in-the-Loop (HITL) in Agentic Chatbot using LangGraph
- 18:48:09 Agentic Chatbot CI/CD Deployment on AWS with Docker & GitHub Actions
- 19:52:05 Deploy Agentic AI Chatbot on Render for FREE with Docker
- 20:06:27 Project: Build Your Own ChatGPT Agent with LLMs, LangGraph, FastAPI, LangSmith, ChromaDB, SQLAlchemy & AWS
- 22:21:17 Project: Build TripMate AI End-to-End: Multi-Agent Travel Planner with Groq, LangGraph, PostgreSQL & FastAPI

🎉 Thanks to our Champion and Sponsor supporters:
👾 @omerhattapoglu1158
👾 @goddardtan
👾 @akihayashi6629
👾 @kikilogsin
👾 @anthonycampbell2148
👾 @tobymiller7790
👾 @rajibdassharma497
👾 @CloudVirtualizationEnthusiast
👾 @adilsoncarlosvianacarlos
👾 @martinmacchia1564
👾 @ulisesmoralez4160
👾 @_Oscar_
👾 @jedi-or-sith2728
👾 @justinhual1290

--

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👥 Are you building an AI software company? Partner with me: https://go.danmartell.com/4pjN7RP

By 2030, the biggest wave of new jobs isn't going to reward people who can chat with AI. It's going to reward people who can build with it.

Right now most people are still typing prompts one at a time, while a small group is quietly building AI agents that run their businesses in the background.

In this video, I show you exactly how I went from doing the work to directing it, and how you can build your first AI agent this week even if you've never touched code.

Watch until the end and you'll walk away with a repeatable way to think about every future agent you build.

Prompts from the video:

Identity files prompt:
"I want to build an AI agent that runs my inbox [your AIM from previous Step]. Create its three identity files: a SOUL file, an IDENTITY file, and a USER file (about me). Ask me any questions you need to fill these in accurately, then write all three."

Manager agent prompt:
"You are my Manager agent. You never do any task yourself. When a job comes in, your only move is to spin up a dedicated sub-agent for that one job, hand it the task, and let it run. One agent, one lane. If a job touches on multiple areas, split it into separate sub-agents, one per area. You coordinate and report back to me."

Specialist agent prompt:
"Your one job is my inbox — nothing else. You never step outside the inbox."

Email voice study prompt:
"Connect to my email and read my last 50 sent messages. Study how I actually write — my tone, my greetings and sign-offs, how long my sentences are, the phrases I use the most. Then write a style guide that captures my voice and tone. To test it: draft a reply to my newest unread email as me, and I'll tell you what's off so you can tighten it."


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IG: @danmartell