Learn the essential AI concepts and basics. We will cover theory and also go over code for building agents. We will cover both no-code tools (Zapier, N8N) and coding frameworks (langchain etc.) for building agents.


Code and assignment: https://resources.codebasics.io/GPpiop

Build your first agent using Zapier: https://youtu.be/Jekzc6BM5_w?si=tHrbHGBPJ8mmJqZv

Air canada chatbot case: https://www.forbes.com/sites/m....arisagarcia/2024/02/

Codebasics AI engineering cohort: https://resources.codebasics.io/RjnPLS

⭐️ Timestamps ⭐️
0:00 Intro
1:01 What is an AI Agent?
7:48 ReAct Loop
8:19 Multi-Agent System
9:07 Multimodal Agent
10:02 Two ways of creating agents
12:30 Workflow vs Agents
13:37 Autonomy and Guardrails
16:12 Evaluation
19:38 Assignment PDF

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.

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🔥Enroll for Agentic AI Course: https://intellipaat.com/agenti....c-ai-systems-design-

🔥𝐁𝐨𝐨𝐤 𝐲𝐨𝐮𝐫 𝐅𝐫𝐞𝐞 𝐌𝐚𝐬𝐭𝐞𝐫𝐜𝐥𝐚𝐬𝐬: https://forms.gle/g5tExa7e54xpYZW97

Master the future of AI with this complete Agentic AI Full Course for Free 2026 covering AI Agents, LangChain, RAG, Embeddings, MCP, Large Language Models, and Agentic AI Design Patterns. This course is designed for beginners, developers, and AI enthusiasts who want to understand how autonomous AI systems work and how modern AI applications are built. 🚀

In this Agentic AI Full Course for Free 2026, you’ll learn the difference between Generative AI and Agentic AI, explore powerful AI frameworks, and understand concepts like embeddings, LLMs, RAG Agents, and Model Context Protocol through detailed explanations. The course follows a practical learning approach to help you build a strong foundation in AI engineering and intelligent automation.

Whether you want to start your AI journey, upgrade your development skills, or explore the next generation of AI systems, this Agentic AI Full Course for Free 2026 gives you a complete roadmap to mastering AI agents and real-world AI workflows. Don’t forget to like, share, and subscribe for more AI tutorials and tech content.

📖 Below are the concepts covered in the video on "Agentic AI Full Course":
00:00:00 – Introduction to Agentic AI Course
00:01:18 – Generative AI vs Agentic AI
00:10:57 – What is a Generative AI System?
01:03:48 – What is LangChain
01:23:32 – AI Agents
01:41:50 – Agentic AI Design Patterns
02:33:37 – Agentic Frameworks & Basics
04:34:25 – Embeddings
05:05:53 – Large Language Model
06:54:04 – RAG Agent
07:52:06 – Model Context Protocol

#agenticai #artificialintelligence #ai #machinelearning #datascience #aitrends #futureofai #aiagents #langchain #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

▶️ Intellipaat Achievers Channel: https://www.youtube.com/@intellipaatachievers

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

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In this video on "What is Agentic AI", we explain the meaning of Agentic AI through the simple story of Emma, a small online business owner. You will see how traditional AI can provide useful ideas and answers, while Agentic AI can understand a goal, create a plan, use tools, take actions, and improve results step by step.

Emma initially uses a normal AI chatbot to get ideas for increasing her weekend sales. Although the AI suggests discounts, social media promotions, and email campaigns, Emma still has to complete every task herself. With Agentic AI, the experience changes. The AI reviews sales information, studies customer queries, identifies popular products, creates campaign plans, writes email drafts, prepares social media captions, recommends posting times, and tracks campaign performance. Simplilearn’s AI certification programs can help learners understand modern AI tools, prompt engineering, AI automation, and the safe use of AI systems in real-world situations.

Watch the complete video to understand how Agentic AI helps people move from ideas to action.

Topics covered:
1. What is Agentic AI?
2. What are AI agents
3. Traditional AI vs Agentic AI
6. AI guardrails
7. Business automation
8. Generative AI and the future of work.

Related Videos:
✅ 1. https://youtu.be/2MHjpOHQNNQ?si=a8lofxiO2g8ONByK
✅ 2. https://youtu.be/DfFoMhfYNLI?si=gxT5Dy5rUZ7OXh1l
✅ 3. https://youtu.be/ia7M-lJiTMY?si=fu8XSxaAjcLIgIaH
✅ 4. https://youtu.be/phkxmDo0TlQ?si=z900jRlscbwFu6iA
✅ 5. https://youtu.be/2R-niMsB0QY?si=UaM46S85L7vVmVoU

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⏩ Check out More AI Videos By Simplilearn: https://youtube.com/playlist?l....ist=PLEiEAq2VkUULyr_

🔥Applied Generative AI Specialization - https://www.simplilearn.com/applied-ai-course?utm_campaign=l_Zg237msTg&utm_medium=Description&utm_source=Youtube
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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
✅ Simplilearn's JobAssist helps you get noticed by top hiring companies
✅ Gain 24/7 access to Simplilearn’s Learning Management System (LMS)

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
✅ Attention Mechanisms
✅ Transformers
✅ GenAI Application Development
✅ LLM Benchmarking
✅ GenAI Governance
✅ AI Image Generation

👉 Enroll Now: https://www.simplilearn.com/applied-ai-course?utm_campaign=l_Zg237msTg&utm_medium=Description&utm_source=Youtube

Want to dive deeper? This curriculum is covered in the following online courses:
- Agentic AI professional education course: https://stanford.io/4zOyjPN
- XCS329 graduate course: https://online.stanford.edu/co....urses/cs329a-self-im

A similar curriculum is covered in XCS329z https://online.stanford.edu/co....urses/cs329z-enginee

Follow along with the course schedule and syllabus: https://cs329a.stanford.edu/

View the course playlist: https://www.youtube.com/playli....st?list=PLangBM27OtE

Video Summary:
This first lecture videoof Stanford's CS329A, Self-Improving AI Agents, taught by Aakanksha Chowdhery and Azalia Mirhoseini on September 22, 2025, opens with an overview of scaling laws that link model parameters, training compute, and dataset size to lower test loss in large language models from GPT-2 through GPT-4. It covers few-shot and zero-shot learning, the emergence of chain-of-thought reasoning in larger models, and the role of instruction tuning and reinforcement learning from human feedback in the development of ChatGPT. The lecture introduces inference-time scaling through the Large Language Monkeys project, which repeatedly samples a model's outputs and selects correct answers with a verifier to improve performance without retraining. It then traces the shift from single-turn chatbots to agent workflows such as prompt chaining, routing, parallelization, and orchestrator-worker patterns, using Claude Code and deep research tools as examples. The session closes with logistics for the course.

Speaker Bios:
Aakanksha Chowdhery
Adjunct Professor of Computer Science, Stanford University

Dr. Aakanksha Chowdhery is pushing the frontier of agentic LLMs, focusing on recursive self-improvement and long-horizon agents that learn and deploy in the real world. She is one of the few researchers globally who has led frontier model training end-to-end, across both dense and mixture-of-experts (MoE) architectures. At Google, she led the 540B PaLM model, the largest densely trained language model in the world at the time. She subsequently drove pre-training and scaling of Gemini's MoE models across multiple generations, and contributed key components to PaLM-E, Med-PaLM, and the Pathways infrastructure underpinning Google's large-model efforts. She went on to build and lead pretraining teams for open intelligence efforts at Reflection and Meta. Earlier, she held research roles at Microsoft Research and Princeton. At Stanford, where she earned her PhD, she teaches CS329A (Self-Improving AI Agents) and serves as Program Chair for MLSys 2026.

Azalia Mirhoseini
Assistant Professor of Computer Science, Stanford University

Azalia Mirhoseini is a co-founder of Ricursive Intelligence, a frontier lab dedicated to recursive self-improvement through AI that designs the chips that fuel it. She is also an Assistant Professor of Computer Science at Stanford University where she directs Scaling Intelligence, a lab focused on developing scalable and self-improving AI systems and methodologies toward the goal of artificial general intelligence. Previously, she spent several years in industry AI labs, including Google Brain, Anthropic, and Google DeepMind, working on the development of Claude and Gemini. Her past work includes Mixture-of-Experts (MoE) neural architectures, now predominantly used in leading generative AI models; AlphaChip, a pioneering work on deep reinforcement learning for layout optimization used in the design of advanced chips like Google AI accelerators (TPUs) and data center CPUs; as well as pioneering research on LLM Test-Time Scaling. Her work has been recognized through the Okawa Research Grant, the Google ML and Systems Junior Faculty Award, MIT Technology Review's 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including WSJ, NYT, Forbes, MIT Technology Review, IEEE Spectrum, WIRED, and TechCrunch.

Code link: https://github.com/krishnaik06..../Agentic-LanggraphCr
LangGraph is built for developers who want to build powerful, adaptable AI agents. Developers choose LangGraph for:

Reliability and controllability. Steer agent actions with moderation checks and human-in-the-loop approvals. LangGraph persists context for long-running workflows, keeping your agents on course.
Low-level and extensible. Build custom agents with fully descriptive, low-level primitives free from rigid abstractions that limit customization. Design scalable multi-agent systems, with each agent serving a specific role tailored to your use case.
First-class streaming support. With token-by-token streaming and streaming of intermediate steps, LangGraph gives users clear visibility into agent reasoning and actions as they unfold in real time.
Learn LangGraph basics¶
To get acquainted with LangGraph's key concepts and features, complete the following LangGraph basics tutorials series:

Build a basic chatbot
Add tools
Add memory
Add human-in-the-loop controls
Customize state
Time travel

Timestamp:

00:00:00 Introduction And Agenda
00:03:37 Langgraph Projects Structure
00:11:15 Building Blocks Of LAnggraph
00:23:47 Building a Basic Chatbot
00:49:05 Building Chatbot With Tools
01:18:37 ReACT Agent Architecture
01:26:22 Adding Memory In Langgraph
01:35:00 Streaming In Langgraph
01:44:58 Human Feedback In the loop
01:52:42 MCP Server Scratch Implementation

This course, from Rola Dali, PhD, provides a comprehensive overview of agentic AI, defining agents as software entities that use LLMs to perceive environments, make decisions, and execute actions to achieve specific goals. It explores the critical distinction between static workflows and dynamic agentic systems, emphasizing how LLMs serve as a reasoning "brain" to decompose tasks at runtime. Through practical Python demonstrations, the course covers essential components like system prompts, tools, and memory, while also comparing architectural patterns such as Supervisor and Swarm. Finally, the session addresses the future of technology by discussing emerging interoperability protocols like MCP and the shifting paradigms of software development in an AI-driven world.

Slides and Labs: https://github.com/rdali/ML105_Agents

Profile: https://www.linkedin.com/in/roladali/

❤️ 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 and Speaker Background
- 0:01:15 A Brief History of Artificial Intelligence (1940s–Present)
- 0:05:43 Traditional Machine Learning vs. Generative AI
- 0:06:35 The Three Pillars of AI: Algorithms, Data, and Compute
- 0:11:08 Specific Tasks vs. General Task Execution
- 0:14:41 Defining Agency and the Spectrum of Autonomy
- 0:18:00 Agentic Milestone Timeline (2017–2026)
- 0:20:31 What is a Generative AI Agent?
- 0:23:04 Agents vs. Workflows: Dynamic Flow vs. Static Paths
- 0:26:18 Pros and Cons of Agentic Systems
- 0:29:59 Patterns and Anti-patterns: When to Use Agents
- 0:32:36 The Core Components of an Agent
- 0:34:55 Choosing the Right LLM for Your Agent
- 0:37:38 Crafting Identity with System Prompts
- 0:39:00 Understanding Memory: Intrinsic, Short-term, and Long-term
- 0:41:26 Enhancing Capabilities with Tools and Actions
- 0:43:09 Hands-on Implementation: From Single LLM Call to Python Agent
- 0:52:18 Adding Memory and History to Your Custom Agent
- 0:54:53 Building Agents with Frameworks (LangChain)
- 0:57:17 The Evolving Landscape of Models and Frameworks
- 1:00:15 Agentic Architectural Patterns: Supervisor vs. Swarm
- 1:01:41 Case Study: Single Agent vs. Supervisor Architecture
- 1:04:48 Deep Dive: Swarm Architecture Performance
- 1:06:08 When to Choose Multi-agent Systems
- 1:09:05 Interface Protocols: MCP, A2A, and AGUI
- 1:12:06 How to Evaluate Agentic Systems (LLM vs. System vs. App)
- 1:13:53 Evaluation Methods: Code-based, LLM-as-a-Judge, and Human
- 1:15:25 Current Challenges: Hallucinations, Cost, and Debugging
- 1:18:15 Real-world Incidents and the AI Incident Database
- 1:21:28 Career Impact: Which Jobs are Most at Risk?
- 1:23:41 Software 3.0: The Evolution of Development Paradigms
- 1:29:00 Weathering the Storm: Strategies for the Future
- 1:33:40 Beyond LLMs: World Models and the Future of AMI
- 1:37:15 Recommended Resources and Closing Thoughts

🎉 Thanks to our Champion and Sponsor supporters:
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--

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In this comprehensive hands-on workshop, Jon Krohn and Ed Donner introduce AI agents, including multi-agent systems. All the essential theory of agentic AI is provided alongside hands-on code demos in Python that feature the critical agent frameworks: MCP, CrewAI and the OpenAI Agents SDK. From design considerations through to practical implementation tips, by completing all four modules in this video, you will have all the knowledge and skills needed to develop and deploy your own multi-agent teams.

The four modules are:
1. Defining Agents (with OpenAI Agents SDK)
2. Designing Agents (with CrewAI)
3. Deploying Agents (with MCP)
4. The Future of Agents

The accompanying slides and code are available in GitHub: github.com/ed-donner/action

Jon Krohn is co-founder and CEO of Y Carrot, a software firm that specializes in designing and implementing agentic AI systems for enterprises. He also serves as ML Practice Fellow at Lightning AI and is the host of SuperDataScience, the data science industry’s most listened-to podcast. He wrote the book Deep Learning Illustrated, an instant #1 bestseller that was translated into seven languages. He lectures at leading universities (Columbia, NYU), keynotes at top global conferences (Web Summit, Collision) and presents content on Bloomberg TV that’s viewed by millions. Jon holds a PhD from Oxford and has been publishing on machine learning in prominent academic journals since 2010.

Ed Donner is renowned for his teaching on LLMs and agentic AI via O'Reilly and Udemy. He’s the co-founder and CTO of Nebula.io, the platform that uses generative AI and other forms of machine learning to source, understand, engage and manage talent. Previously, Ed was the founder and CEO of AI startup untapt, an Accenture Fintech Innovation Lab company that was acquired in 2020. Before that, Ed was a Managing Director at JPMorgan Chase, leading a team of 300 software engineers in Risk Technology across three continents, the culmination of a 15-year technology career on Wall Street. Ed holds a patent for a deep learning matching engine issued in 2023 and a master's in physics from Oxford.

This high-quality footage was shot live during a packed house at the Open Data Science Conference (ODSC) East in Boston on May 14th, 2025. The director of photography was Lucie McCormick and media editor was Mario Pombo.

Two bonus goodies below:

Follow this link to get Ed's 17-hour "Complete Agentic AI Engineering Course" on Udemy at a discounted rate (as well as coupon links to all of his Udemy courses): https://edwarddonner.com/2025/....05/28/connecting-my-

Use this link to skip the waitlist for Lightning AI Studios and get 15 free GPU-compute credits every month: https://lightning.ai/?inviteCo....de=Agentic-AI-worksh

Next: Your first AI Employee: https://youtu.be/jYbzKYwdy24

In this video, I break down what Agentic AI really is, beyond the hype and buzzwords. While most people think of AI as just chatbots like ChatGPT, the next evolution - Agentic AI - is quietly transforming how we interact with technology.

I explain in simple terms how Agentic AI systems can actually take initiative, complete complex tasks autonomously, and solve problems without constant human guidance. You'll understand the key differences between reactive AI tools and truly agentic systems that can plan, adapt, and achieve goals.

Whether you're a developer, business leader, or just curious about AI's future, this concise explanation gives you the foundation to understand why Agentic AI represents such a significant leap forward in artificial intelligence.

#AgenticAI #ArtificialIntelligence #AIExplained #FutureOfAI #AgentBasedAI #AITechnology #TechTrends #MachineLearning #BusinessAI #AIAgents

🚀 Complete Agentic AI Course for Beginners in Telugu

Code files:
LangGraph code:
https://drive.google.com/file/....d/1l6N1T06ghWX-PpTSR

CrewAI code:
https://drive.google.com/file/....d/13QghlEBku_OCM5kYi

In this 3-hour full course, you will learn Agentic AI from scratch and understand how to build your own AI Agents step by step.

This course is explained in Simple Telugu and is perfect for beginners who want to learn modern AI Agent development.

What You Will Learn

✅ What is an AI Agent?
✅ AI Agent vs Chatbot
✅ How AI Agents think, plan, use tools and complete tasks
✅ LangGraph explained with practical workflow
✅ MCP — Model Context Protocol
✅ A2A — Agent-to-Agent Protocol
✅ CrewAI multi-agent systems
✅ OpenAI Agents SDK overview
✅ How to build AI Agents from scratch
✅ Real-world Agentic AI use cases

Topics Covered

🤖 AI Agents
🔷 LangGraph
🔗 MCP
🤝 A2A Protocol
👥 CrewAI
⚡ OpenAI Agents SDK
🧠 Agentic AI Architecture
💻 Practical AI Agent Projects

If you want to learn AI, Machine Learning, Generative AI and Agentic AI in Simple Telugu, subscribe to AI School of India.

Let’s make AI learning simple and practical 🚀

Hashtags

#AgenticAI #AIAgents #LangGraph #CrewAI #MCP #A2A #OpenAIAgentsSDK #GenerativeAI #ArtificialIntelligence #AIinTelugu #AISchoolOfIndia #MachineLearning #DataScience

🔥Advanced Certification in Agentic AI Engineering: https://www.edureka.co/agentic-ai-training-course
🔥 Integrated MS+PGP Program in Data Science & AI https://www.edureka.co/dual-ce....rtification-programs

In this video, we dive into the exciting developments and advancements that are set to shape the industry in 2025. 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 2025. Whether you're an AI enthusiast, a business leader, or simply curious about the future of technology, this video is for you.

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

✅Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
📝Feel free to share your comments below.📝
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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. It is widely utilized in robotics, virtual assistants, self-driving cars, and sophisticated decision-making processes.

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

Learn Agentic AI using a popular framework langgraph. In this agenti ai tutorial for beginners, we will start with agentic AI basics and then we will dive deeper into langgraph by covering a wide range of topics.

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Code: https://github.com/codebasics/....langgraph-crash-cour

00:00 Introduction
00:40 Agentic AI Basics
00:00 What is Langgraph
07:41 Langchain vs Langgraph
13:08 Installation and Setup
19:00 Simple Graph
31:36 Graph with Condition
36:55 Chatbot in Langgraph
45:38 Chatbot with Tool
56:03 Memory
1:05:05 Tracing with Langsmith
1:10:11 Human in the Loop

Do you want to learn technology from me? Check https://resources.codebasics.io/rS9B09 for my affordable video courses.

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This video contains a practical roadmap to learn agentic AI using free learning resources and step by step study plan.

Roadmap Link: https://resources.codebasics.io/Y0HtDw

Gen AI Bootcamp: https://codebasics.io/bootcamp....s/gen-ai-data-scienc

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

Learn more about Agentic AI here → https://ibm.biz/BdbhXb

Ever hit a wall with your biggest LLM? 🚧 Shad Griffin shows how Agentic AI, AI agents, and prompt engineering turn complex problems into smart, multi‑step solutions powered by machine learning. Discover how this workflow helps developers build more accurate AI results.

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

#agenticai #llm #aiagents #promptengineering #machinelearning

Checkout the roadmap: https://www.krishnaik.in/ai-roadmaps
Agentic AI involves creating autonomous systems that use Large Language Models (LLMs) to plan, reason, and act to achieve goals, rather than just generating content. To learn this, focus on mastering frameworks like LangGraph, CrewAI, and AutoGen to build multi-agent workflows, and focus on agentic RAG for data access. Key skills include planning, tool usage, and human-in-the-loop interaction.
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visit : https://krishnaik.in/liveclasses

Learn more about AI Agent Frameworks here → https://ibm.biz/~M1qnwGHnE

Too many agentic AI frameworks and no clear starting point. 🤯 Meenakshi Kodati breaks down agentic AI frameworks like LangChain, AutoGen, and CrewAI across workflows, multi-agent systems, and production use cases. Learn how to choose the right framework based on your system design and real-world needs.

AI news moves fast. Sign up for a monthly newsletter for AI updates from IBM → https://ibm.biz/~YOhBiYsX4

#agenticai #aiagents #langchain #aiframeworks

Learn more: https://stanford.io/4iHlGQp

Build modern AI agents with Stanford's graduate-level curriculum, adapted for working professionals.

Taught by Stanford Adjunct Professor Chowdhery and Assistant Professor Mirhoseini, this Agentic AI program takes you beyond basic LLM prompting to agents that reason, plan, use tools, and improve themselves through interaction with their environment.

What you'll learn:
- Design agentic systems for complex, multi-step real-world tasks
- Apply test-time scaling to boost LLM performance
- Use self-improvement methods: verifiers, feedback loops, RL, and search
- Build agents that use tools and take actions effectively
- Add retrieval and long-term memory to LLMs
- Develop planning and multi-step reasoning
- Evaluate agent performance with robust frameworks

Drawing on Stanford research and the latest advances in the field.

About the Instructors
Stanford Engineering Adjunct Professor Dr. Aakanksha Chowdhery led end-to-end training of the 540B PaLM model (the largest densely trained language model in the world at the time) and drove pre-training and scaling of Gemini's mixture-of-experts models. Stanford Assistant Professor Azalia Mirhoseini co-developed the MoE architectures now used in nearly every frontier model, created AlphaChip (the RL method behind Google's TPU designs), and pioneered LLM test-time scaling; she worked on both Claude and Gemini.