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Most people still use AI like a better search box, but the real shift is AI agents, systems that decide the next action, not just the next word.

I explain the difference between prompts and agents using ARR, show what’s happening “under the hood” with four roles, and map how agents adapt through an OODA loop when workflows break.

I also cover why agents fail in real life: they amplify vague thinking and bad processes, so you need a GPS check before automating anything.

The opportunity isn’t broad intelligence; it’s narrow, specific agents that solve repeated, hated tasks, as output gets cheap and judgment, taste, and standards become more valuable.

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Explore the future of artificial intelligence with Agentic AI! In this video, we dive into the exciting developments and advancements that are set to shape the industry in 2026. We will learn what Agentic AI is and how it goes beyond traditional AI by acting with purpose and autonomy. You’ll discover 5 powerful secrets behind Agentic AI—from multi-agent collaboration to real-world tool integration and ethical guardrails. By the end, you’ll understand how Agentic AI is transforming industries and why it’s the future of intelligent systems.

Join us as we discuss the latest trends, innovations, and predictions for Agentic AI in 2026. Whether you're an AI enthusiast, a business leader, or simply curious about the future of technology, this video is for you. Stay ahead of the curve and discover what's next for Agentic AI!

00:00:00 Introduction
00:17:13 What is Agentic AI?
00:36:19 Agentic AI vs Generative AI
00:46:07 Agentic AI Roadmap
00:52:18 Introduction to Artificial Intelligence
00:58:37 Introduction to Deep Learning
02:28:29 Artificial Neural Network
03:08:09 Transformers Explained Using Generative AI
03:09:37 Transformers Neural Networks Explained
03:21:52 What are Large Language Models?
03:38:17 LLM vs SLM
03:41:59 What is LangChain?
03:59:35 What is RAG?
04:22:07 LLMOps: The Future of AI Development
04:29:57 Prompt Engineering
04:42:41 Natural Language Processing (NLP) & Text Mining using NLTK
05:22:21 KNN Algorithm using Python
05:45:23 Alibaba’s Qwen 2.5-Max Just Beat GPT-4 & DeepSeek?
05:49:47 DeepSeek vs OpenAI: Who Wins the AI Race?
06:02:06 Deep Learning Interview Questions

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- - - - - - - - - - - - - -

What is Agentic AI?
Agentic AI refers to artificial intelligence systems that can autonomously make decisions, take actions, and pursue goals with minimal human intervention. Unlike traditional AI, which adheres to predetermined rules, agentic AI may dynamically adapt to new situations.

What are the prerequisites for this Agentic AI Training Course?
In order to complete this course successfully, participants need to have a basic understanding of the Python programming language, machine learning, deep learning, natural language processing, generative AI, and prompt engineering concepts.

Who should take this AI Agents Training Course?
The Agentic AI online Training Course is ideal for AI enthusiasts, developers, and professionals looking to build autonomous AI agents. It is best suited for LLM Engineers, Generative AI Engineers, AI Research scientists, AI/ML practitioners, and freshers who want to leverage Agentic AI for automation and decision-making.

For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18885487823(toll-free).

#agenticai #aiagents #agenticaicourse #generativeai

Best courses to learn all about AI agents and AI engineering:
DataCamp's Associate AI Engineer for Developers Track - https://datacamp.pxf.io/X4reD4
Datacamp's AI Agents Fundamentals Track - https://datacamp.pxf.io/1GgN0a
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Everyone is talking about AI agents. Almost nobody explains what they actually are or how to build one. That changes right now.

Here's the honest one-sentence version: an AI agent is a language model that can use tools, running in a loop until it finishes a job. That's it. Everything else is just details — and in this video I break all of those details down clearly, then show you how to build the same agent in four completely different ways.

Want to make real money with coding? I share high-signal insights on careers, monetization, and leverage in my free newsletter. Join here and get my guide How to Make Money With Coding instantly: https://techwithtim.net/newsletter

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⏳ Timestamps ⏳
00:00 | Overview
00:52 | What Are Agents
01:53 | The Key Building Blocks
07:15 | The Landscape/Options
08:25 | DataCamp
09:52 | Tier 1 - No Code
12:36 | Tier 2 - Low Code
15:02 | Tier 3 - Agent Harness
17:44 | Tier 4 - Full Code
19:54 | How to Pick?

Hashtags
#AIAgents #ArtificialIntelligence #AITutorial

UAE Media License Number: 3635141

Follow the codelab → https://goo.gle/3Q5TSt3
GitHub repo → https://goo.gle/4fsahT8
Google Agent Development Kit (ADK) → https://goo.gle/3Q3enqf

At the simplest level, an AI agent doesn’t just answer—it decides and takes action. In this video, Smitha goes beyond basic chatbots and demonstrates how to build a fully autonomous, self-correcting multi-agent system from scratch using Google’s Google Agent Development Kit (ADK).


First, Smitha breaks down the theory behind modern agents: the ReAct Framework (reasoning and acting) and the 3 main agent patterns (sequential, reactive, and planning). Then, she jumps straight into Python to build a practical *Blog Writing Agent*. Watch along and learn how to combine *Planner* and *Writer* agents with validation checkers and loop agents to create an AI that catches its own mistakes and automatically retries until it gets it right.

Chapters:
00:00 - AI Agents Explained
01:05 - The ReAct Framework Explained
02:15 - The 3 Types of AI Agents (Sequential, Reactive, Planning)
03:30 - Project Overview: The Auto-Correcting Blog Writer
04:15 - Setting Up Google ADK & UV
04:50 - Coding the Planner Agent
05:40 - Adding Auto-Correction (Validation Checkers & Loop Agents)
06:50 - Coding the Blog Writer & Root Agent
08:20 - Testing the AI in the ADK Web UI
09:40 - What's Next? (Connecting to MCP Servers)

More resources:
ReAct Paper → https://goo.gle/4oa1oQ9

🔗 Connect with Smitha online:
YouTube → https://goo.gle/Smitha-on-YouTube
Linkedin → https://goo.gle/Smitha-on-LinkedIn
X → https://goo.gle/Smitha-on-X

#AIAgents #GoogleADK #PythonTutorial #SoftwareEngineering #MachineLearning #LLMs

Watch more Modern AI Agents: From Theory to Production → https://goo.gle/Learn-with-Smitha
🔔 Subscribe to Google Cloud Tech → https://goo.gle/GoogleCloudTech

#AIAgents #Gemini

Speaker: Smitha Kolan
Products Mentioned: Agent Development Kit, Gemini

Generative AI vs AI Agents vs Agentic AI Explained | Complete Beginner's Guide (2026)

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Original Video Link: https://youtu.be/DLR0SN2sB4Q?si=8TODgGelnle8wSQi

👉About This Video:
This video has been independently edited, redesigned, and transformed. It contains original commentary, analysis, and educational insights added for the purpose of learning and understanding.
The intention of this content is to educate, inspire, and provide valuable knowledge.



📢 Fair Use Disclaimer:
This video is created under Section 107 of the Copyright Act 1976, which permits fair use for purposes such as criticism, comment, teaching, research, and education.
The content has been transformed, repurposed, and presented with new perspective and value.

Tags:-
#neerajwalia #ai #generativeai #aiagents #agenticai #chatgpt #artificialintelligence #aitutorial #tech #machinelearning #aiexplained

"🔥Microsoft Applied Agentic AI: Systems Design & Impact - https://www.simplilearn.com/agentic-ai-course-training?utm_campaign=kk_uZWQAF3A&utm_medium=DescriptionFF&utm_source=Youtube
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This video on Agentic AI Full Course 2026 by Simplilearn will help you learn Agentic AI from beginner to advanced level and understand how autonomous AI systems can plan, reason, make decisions, and execute complex tasks with minimal human intervention. The course begins with an introduction to Agentic AI and explains how AI agents differ from traditional AI models by combining reasoning, planning, memory, tool usage, and goal-oriented execution. You will learn key concepts such as AI agents, large language models (LLMs), prompt engineering, Retrieval-Augmented Generation (RAG), tool calling, agent workflows, multi-agent systems, memory management, and orchestration frameworks.

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

00:00:00 - Introduction To Agentic AI Full Course 2026
00:03:54 - Basics of Agentic AI
00:08:06 - Core Concepts of Agentic AI
00:15:15 - Agentic AI Frameworks & Libraries
00:22:09 - Memory and Long-Term Context
00:30:01 - Planning & Decision Chains
00:36:25 - Advanced Use-Cases of Agentic AI
00:39:48 - Agent Evaluation and Prompt Optimization
00:43:50 - Risks, Ethics, and Future of Agentic AI
00:47:27 - Hands-On Project
04:08:49 - Top Hacks To Speed Up Your AI Coding Workflow
05:51:53 - Generative Adversarial Tutorial
06:52:32 - Emergent AI App Builder Tutorial For Beginners
07:03:58 - Top 5 AI Automation Tools 2026
07:15:15 - How To Build WhatsApp AI Agent Using n8n
08:14:34 - Mobile App Development In 12 Minutes Using AI
09:40:37 - LangSmith Tutorial For Beginners 2026
10:14:58 - LangChain Tutorial For Beginners 2026
11:00:54 - LangGraph vs LangChain vs LangFlow vs LangSmith 2026
11:10:27 - Build LLM Apps Using Langchain
12:37:42 - Build a Chatbot Using Langchain
13:43:40 - What Is Miro And How To Use It?
13:59:30 - MetaGPT Tutorial For Beginners
14:56:55 - Streamlit Tutorial For Beginners
15:23:37 - Prompt Engineering Tutorial For Beginners
15:57:03 - LLM scale and deployment
16:07:26 - Large language models overview
16:14:53 - Features, tokens, and model choice
16:21:24 - AI tools across industries
16:26:32 - Resume and job matching demo
16:37:05 - Prompt engineering fundamentals
16:54:41 - Everyday prompt use cases
17:04:21 - Multimodal AI capabilities
18:19:50 - Key takeaways and next steps
18:26:41 - Effective prompt design principles
19:08:36 - Gen AI For Data Analytics
20:53:11 - Data integrity and EDA setup
21:42:42 - Data modeling with genAI
22:07:34 - Modeling techniques and use cases
23:46:43 - Future trends and wrap-up
24:03:57 - Gen AI Powered SQL For Data Analytics
25:09:42 - ER model fundamentals
26:26:15 - Database commands and basic setup
27:04:37 - Table creation and constraints
28:33:57 - Query logic and aggregation
29:00:27 - SQL functions introduction
30:19:37 - Using ChatGPT for SQL practice
30:22:09 - Agentic AI Projects For Beginners 2026
30:46:15 - What Is GenAI?
30:50:35 - How To Become Generative AI Engineer?
31:00:40 - Generative AI Vs Agentic AI Vs AI Agents
32:37:26 - LLM As Operating System
33:30:16 - LLM Benchmarking
34:46:06 - Prompt engineering with GPT-4
35:41:26 - Vibe coding tools comparison
36:06:01 - Generative AI learning roadmap
36:58:56 - AI for research and content
37:22:52 - Data insights and AI workflows
38:12:03 - Gemini CLI versus Claude Code
38:37:03 - ChatGPT attempts LeetCode problems
40:56:55 - Prompt library foundations
41:11:56 - Reasoning with o1 models
42:57:42 - Prompt library recap
43:56:35 - How To Build AI Automations Using n8n
44:26:47 - How To Build a Self Learning AI Agent
46:20:12 - Top 30 Generative AI Interview Questions 2026

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If you want to master AI agents in 2026 and actually build systems that can reason, plan, and take action on their own- this video is your complete roadmap.

I walk you through exactly what you need to learn, in what order, and which free resources will actually get you there- without wasting months chasing hype.

The space feels overwhelming right now. Everyone's throwing around words like agents, autonomy, multi-agent systems, tool use, planning, memory- and it all sounds exciting, but also kind of chaotic.
You're not too late. But you do need a plan. That's exactly what this video gives you.

Chapters:
00:00 – Why You Need a Roadmap for AI Agents
01:08 – Prerequisites: Python, APIs, ML Fundamentals
03:25 – Month 1: Foundations & Architecture
05:17 – Month 2: Agent Frameworks & Memory
06:55 – Month 3: Tools, APIs & Multi-Agent Systems
08:06 – Month 4: Evaluation, Safety & Deployment
08:55 – Month 5-6: Specialization, Advanced Topics & Capstone
10:12 – Final Advice & Resources

Free Resources Mentioned:

Prerequisites-
Google's Python Class: https://developers.google.com/edu/python
Python for Everybody (Charles Severance): https://www.py4e.com/

Month 1: Foundations

Hugging Face Agents Course: https://huggingface.co/learn/agents-course

Month 2: Frameworks & Memory

LangGraph Documentation: https://www.langchain.com/langgraph
CrewAI Documentation: https://docs.crewai.com/

Month 3-4: Tools, Multi-Agent & Production

OpenAI Function Calling Guide: https://platform.openai.com/do....cs/guides/function-c
LangSmith (Evaluation): https://www.langchain.com/langsmith

Month 5-6: Advanced & Capstone

Berkeley LLM Agents Course: https://llmagents-learning.org/f24

Drop a comment: Where are you in your AI agents journey? Just starting, already building, or stuck somewhere in the middle?

This is a complete course on learning Generative ai and agentic with Langchain and Langgraph. We have included all the topics from RAG, vectorless rag, Deep agents, Guardrails, LLM Evaluation and LLM Gateways Techniques
Github Links
Langchain : https://github.com/krishnaik06..../Langchain-V1-Crash-
Langgraph: https://github.com/krishnaik06..../Agentic-LanggraphCr
RAG: https://github.com/krishnaik06/RAG-Tutorials
Vectorless RAG: https://github.com/krishnaik06/RAG-Tutorials/blob/main/PageIndex_Vectorless_RAG_CrashCourse%20(1).ipynb
Deep Agents: https://drive.google.com/file/....d/1SVjvgqvKfF-FPAIqp
Guardrails : https://github.com/krishnaik06..../Langchain-V1-Crash-/blob/main/updatedlangchain/langchain_guardrails_crash_course.ipynb
LLM Evals : https://github.com/krishnaik06/RAG-Tutorials/blob/main/1-rag_evaluation.ipynb
LLM Gateways: https://github.com/krishnaik06..../Langchain-V1-Crash-/blob/main/llm_gateway_tutorial.ipynb

Timestamp
00:00:00 Introduction
00:02:31 Langchain Course
02:35:12 Langraph Course
05:02:29 RAG Course
07:10:43 Vectorless RAG
08:02:11 Deep Agents
08:45:43 Guardrails
09:22:55 LLM Evaluation
10:30:25 LLM Gateways
-------------------------------------------------------------------
Learn from us visit https://krishnaik.in/liveclasses

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You're probably using Claude like a chatbot. Same model, same subscription — but agents can open your browser, run 10 tasks at once, and finish a week of work before lunch.

This is the complete guide to agentic AI in 2026: how to create AI agents, how agents actually work, which platforms are worth your time, and the prompt structure that makes them actually deliver.

▶ WHAT'S IN THIS VIDEO
• What separates a chatbot from a real AI agent (LLM + tools + memory + goals loop)
• Claude Code — Anthropic's reasoning agent, and why it's not just for coding
• OpenAI Codex — lowest-friction entry if you already pay for ChatGPT
• OpenClaw — open-source agent that lives inside Telegram, WhatsApp, iMessage
• Google Antigravity — visual agent built on Gemini, best for front-end and design
• Prompt Contracts: Goal → Constraints → Format → Failure (the 4-part structure that stops agents from going off the rails)
• Memory files: teach your agent once and never repeat yourself

⏱️ TIMESTAMPS:
00:00 - The most expensive AI mistake right now
01:21 - What an AI agent actually is
03:30 Claude Code — install & first run
06:45 OpenAI Codex — install & first run
09:07 OpenClaw — install & first run
12:25 Google Antigravity — install & first run
16:44 Prompt Contracts: the 4-section structure
21:53 Memory files: teach once, never repeat

#AIAgents #ClaudeCode #AIAutomation #Brevo

Nick agreed to personally set up your Hermes agent on Orgo in a 15 min call: https://startup-ideas-pod.link/orgo_ai

I sit down with Nick from Orgo to break down exactly how to run a one-person AI agent business that can realistically clear a few million dollars a year. Nick walks through the offer, the verticals worth chasing, the full software stack, and the live setup of an agent that manages other agents. We focus on tactics over theory, with specific tools, pricing, and the playbook for landing customers as a solopreneur. By the end, anyone with solid AI fluency will have a clear path from offer design to fulfillment.

Timestamps
00:00 – Intro
02:54 – Designing the AI Agent Business Offer
06:38– Selling an AI Employee, Not an Agent
07:26 – Industries to Target (and Two to Avoid)
14:54 – Content Is Overpowered and How to Get Customers
17:51 – The Customer-Facing Tool Stack
20:49 – Building Agents Stack
25:51 – Model Picks: GPT 5.5, GLM 5.1, Kimmy, Opus 4.7
27:08 – Nick’s Stack
28:14 – Why Obsidian Is the Second Brain Layer
30:22 – Live Walkthrough: Spinning Up a Cloud Computer in Orgo
33:53 – Cloud Computers vs. Mac Minis
38:37 – Building Agents and Structuring Workspaces for Customers
43:56 – Watchdogs, Observability, and Reliability
45:28 – Closing Thoughts on the Solopreneur Era

Key Points

* Sell unlimited agents, unlimited usage, and unlimited support to remove friction; most customers actually use one to three agents.
* Avoid healthcare and finance to start; focus on legacy verticals like marketing, law, insurance, manufacturing, wholesale, and real estate.
* OpenClaw agents go for around 5K a month; Hermes agents can go for 10K a month.
* The full stack: Granola, Trello, Loom, Superhuman, Asana, Codex, Hermes, Orgo, Composio, Agent Mail, and Obsidian.
* GPT 5.5 is the recommended default model for tool calling; GLM 5.1 and Kimmy work for lighter tasks; Opus 4.7 fits long-horizon coding.
* Use agents to set up other agents — pair Cloud Code or Codex with MCPs like Perplexity, Context7, and X MCP for live docs.

Numbered Section Summaries

1. The Solopreneur Agent Opportunity I open with Nick's premise that AI fluency itself is a rare, monetizable skill. Roughly 99% of the market sits behind on AI, so anyone who can stand up Claude Code, Hermes, or OpenClaw has leverage that businesses will pay real money for. The episode is framed as a tactical A-to-Z playbook rather than an idea-of-the-week pitch.

2. The Anti-Friction Offer Nick argues the winning offer is unlimited agents, unlimited usage, unlimited monitoring, security, and ongoing changes for around 5K a month. The trick is that customers think they need 10 or 100 agents, while in reality one to three handle the bulk of the work. Removing token-talk and credit-talk preserves the magic and shortens time to yes.

3. Picking the Right Vertical Healthcare and finance get flagged as too regulated for a solo operator. Instead, Nick recommends marketing agencies, law firms, insurance agencies, manufacturers, wholesalers, and real estate. These are large legacy industries hungry to become AI native, with executive-level pain that abstracts cleanly across companies.

4. Niching Down the Right Way I push on the "diverge then converge" framework: try several verticals, see where the market pulls you, then go sub-niche by geography or by professional type (commercial real estate in Florida, matrimonial law, etc.). Going specific lets you craft an offer that makes a buyer feel personally addressed.

7. Models and Why They Matter GPT 5.5 is the recommended default for Hermes and OpenClaw because it is efficient with tool calls and the OpenAI paid plan is generous. GLM 5.1 from Z.AI is the top open-source pick for cheaper light tasks, with Kimmy close behind.

8. Live Build, Reliability, and Closing Nick walks through Orgo live: a workspace per customer, a cloud computer per agent, and a Telegram-controlled meta-agent that can install Hermes, manage 27 VMs, and patch problems on the fly. We cover MCPs that give agents up-to-date setup context the value of spawning parallel sub-agents for research, and the importance of watchdogs and email-based observability so issues get caught before customers feel them.

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Youtube: https://www.youtube.com/@nickvasiles
Instagram: https://www.instagram.com/nickvasilescu/
Personal Website: https://www.nickvasilescu.com/

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Instagram : https://www.instagram.com/shradhakhapra
LinkedIn : https://www.linkedin.com/in/shradha-khapra/

Welcome to this video course on LangGraph, the powerful Python library for building advanced conversational AI workflows. In this course, Vaibhav Mehra will teach you how to design, implement, and manage complex dialogue systems using a graph-based approach. By the end, you'll be equipped to build robust, scalable conversational applications that leverage the full potential of large language models.

Code: https://github.com/iamvaibhavm....ehra/LangGraph-Cours

Vaibhav Mehra on LinkedIn: https://www.linkedin.com/in/vaibhav-mehra-main/
Vaibhav on Instagram: https://www.instagram.com/iamvaibhavmehra/

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

⭐️ Contents ⭐️
0:00:00 Introduction
0:01:24 Type Annotations
0:08:35 Elements
0:18:39 Agent 1 Intro
0:21:08 Agent 1 Code
0:32:22 Agent 1 Exercise
0:33:07 Agent 2 Intro
0:34:02 Agent 2 Code
0:43:27 Agent 2 Exercise
0:44:29 Agent 3 Intro
0:45:18 Agent 3 Code
0:54:04 Agent 3 Exercise
0:55:36 Agent 4 Intro
0:56:37 Agent 4 Code
1:13:09 Agent 4 Exercise
1:14:14 Agent 5 Intro
1:15:45 Agent 5 Code
1:29:19 Agent 5 Exercise
1:31:01 AI Agent 1 Intro
1:32:49 AI Agent 1 Code
1:42:54 AI Agent 2 Intro
1:43:58 AI Agent 2 Code
2:03:20 AI Agent 3 Intro
2:05:00 AI Agent 3 Prerequisite
2:12:28 AI Agent 3 Code
2:28:46 AI Agent 4 Intro
2:30:59 AI Agent 4 Code
2:52:09 RAG Agent Intro
2:53:01 RAG Agent Code
3:06:15 RAG Agent Testing
3:09:04 Course Outro

🎉 Thanks to our Champion and Sponsor supporters:
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👾 David MG
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👾 Nattira Maneerat
👾 Justin Hual

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In this video, you'll learn exactly how to build five different AI agents across four different platforms and start selling them to businesses. I walk you through my exact experience scaling an AI agency to six figures in 14 months so you can turn technical skills into a highly profitable business.

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Timestamps
00:00 - Introduction
01:27 - Foundations
30:20 - Build 1 (inspo by Eddie Chen)
59:17 - Build 2
01:33:07 - Build 3
02:04:39 - Build 4
02:49:24 - Build 5
03:25:10 - Monetization

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In this video, I break down a complete AI automation full course for beginners in 2026, explaining what is AI automation and how it works. You’ll learn the fundamentals in this practical AI automation tutorial, including how AI agents fit into modern automation workflows and exactly how to get started with AI automation. Whether you're looking for a step-by-step AI automation guide or want to master AI automation for beginners, this covers everything you need to get started.

00:00 - Intro: Stop Wasting Time on Repetitive Work
00:33 - Building Your First AI Automation Agent Step by Step
01:31 - Free Base44 AI Agent Masterclass
01:55 - Chatbots vs AI Agents: The Key Difference
04:09 - Build 1: Automated Email Management Agent
06:49 - Giving Your AI Agent Custom Business Knowledge
09:41 - Build 2: Instant Lead Follow-Up Agent
10:33 - Connecting Google Sheets for Lead Data
12:22 - Build 3: Weekly Sales Reporting Agent
13:52 - Sending Automated AI Reports to Telegram
15:06 - Why Traditional Automation Builders Fail Beginners
16:40 - Integrations: Connecting Gmail, Calendar & Analytics
18:50 - Build 4: Personal AI Travel Planning Agent
20:09 - Build 5: Automated Customer Support Agent
23:22 - Advanced Features: Adding Memory to AI Agents
25:05 - Adding Conditional Logic & Decision-Making
27:09 - Conclusion: Scaling Your Automation Workflows

For inquiries: Mikey (at) ytmedia.group

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
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👾 @CloudVirtualizationEnthusiast
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👾 @martinmacchia1564
👾 @ulisesmoralez4160
👾 @_Oscar_
👾 @jedi-or-sith2728
👾 @justinhual1290

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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

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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.

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Skills Covered
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