Free Agentic AI Course Online: Learn with AI Tutor & Certificate
Over about 20 hours and 86 video lessons, this free agentic AI course teaches you to build and deploy intelligent systems. You'll start by demystifying machine learning, then progress to mastering LangChain, LangGraph, and CrewAI to design complex multi-agent architectures.
What you'll learn in this Agentic AI course
Five concrete outcomes you'll walk away with, each mapped to modules in the curriculum below.
Understand and differentiate between AI agents, assistants, and agentic systems, and apply strategic frameworks for AI integration.
Master advanced prompting techniques and reasoning models to enhance AI interactions and handle ambiguity effectively.
Develop and deploy production-ready AI agents using various frameworks and tools, including LangChain, LangGraph, and CrewAI.
Implement and manage agent memory, state management, and data preparation for effective AI operations.
Design and build multi-agent systems with advanced architectures, including RAG pipelines and context engineering, while ensuring security and performance optimization.
Agentic AI course syllabus
01 Module 1: Foundations of Agentic AI & LLMs
- Demystifying AI, Machine Learning, and Generative AI
- Understanding How Large Language Models (LLMs) Work
- Defining the AI Agent: Core Concepts and Capabilities
- Contrasting AI Agents with AI Assistants
- Distinguishing Between Agentic Systems and Simple Workflows
- The Agent Readiness Framework and Strategic Pillars
- Technical, Organizational, and Governance Pillars
- Assessment Walkthrough and Actionable Next Steps
- Steps 1 & 2: Discovery and Prioritization for AI Integration
- Steps 3-5: Process Mapping and Prototyping AI Solutions
- Steps 6 & 7: Safeguarding, Launching, and Managing Expectations
- Recognizing and Mitigating the Risks of LLMs
- Investigating Why Large Language Models Hallucinate
02 Module 2: The Agentic Mindset: Advanced Prompting and Reasoning
- Mastering Prompt Engineering for Developers
- Applying Advanced Prompting Methods (RAG, CoT, ReAct)
- Understanding Large Reasoning Models (LRMs)
- The Power of Intent: Programming by Example
- Advanced Intent and Agentic Interaction Patterns
- Evaluating Agent Interactions and Handling Ambiguity
- Inspection, Memory, and the Future of Agentic AI
- Establishing a Framework for Benchmarking AI Prompts
03 Module 3: A Developer's Guide to Agentic Frameworks
- The AI Agent Landscape: News, Concepts, and Frameworks
- Production-Ready Agents: A Deep Dive into ADK and Advanced Topics
- Comparing LangChain and LangGraph for Agent Development
- Getting Started with CrewAI for Collaborative Agents
- Introduction to Autogen and Agentic Frameworks
- Environment Setup and Azure OpenAI Configuration
- Building and Running Your First Multi-Agent System
- Creating Agents with Google's Agent Development Kit (ADK)
04 Module 4: Empowering Agents with Tools, Memory, and Data
- Understanding and Implementing Function Calling
- Practical Demo: Integrating External APIs with Gemini and Cloud Run
- Implementing and Enhancing Tools in Autogen with Pydantic
- Deep Dive into Agent Memory and State Management
- Implementing Short-Term and Long-Term Memory with ADK
- Preparing and Structuring Data for LLMs
- Understanding Data Lakehouse Architecture for AI
05 Module 5: Advanced Architectures: RAG and Multi-Agent Systems
- Understanding Retrieval-Augmented Generation (RAG)
- Building a RAG Pipeline: Data Ingestion and Vector DBs
- Building a RAG Pipeline: Advanced Retrieval and Querying
- The Agent Industry Pulse: Software 3.0 and Context Engineering
- Architectural Patterns for Multi-Agent Systems
- Understanding Deep Agents: Planning and Long-Horizon Task Management
- Implementing Agent Planning with Todo List Middleware
- Managing Agent Context with Summarization Middleware
- Implementing Agent Planning with Todo List Middleware
- Hands-On: Building a Multi-Agent System with LangGraph
- Introduction to Autogen Group Chat and Agent Setup
- Executing Group Chats and Handling Sequential Tasks
06 Module 6: Productionizing Agents: Deployment, Evaluation, and Security
- Foundations: From DevOps to MLOps Architecture
- Operationalizing Generative AI: The GenAIOps Framework
- Agent Fundamentals: Design, Evaluation, and Tooling
- Advanced AgentOps: CI/CD, Memory, and Multi-Agent Systems
- Why and How to Deploy AI Agents on GKE
- Securing Code Execution with the GKE Agent Sandbox
- Optimizing Agent Performance with Pod Snapshotting
- Fundamentals of Agent Evaluation: Concepts and Methods
- Advanced Evaluation: Scaling, Synthetic Data, and Multi-Agent Systems
- The Challenge of Defining AI Agent Quality
- Practical Use Cases and Collaborative Workflows
- Platform Demo: Scenario Testing, Skills, and Q&A
- The Evolution and Importance of AI Evaluation
- Developing a Collaborative Evaluation Strategy and Framework
- Advanced Techniques: Agent Simulations and the Test Pyramid
- Implementation, Justification, and Future of Agent Evals
- Evaluating and Debugging Non-Deterministic AI Agents
- Product Evaluations for AI Applications in Three Simple Steps
- Agent Middleware: Implementing Guardrails and Limits
- Implementing Model Call Limits for Cost Control
- Building Resilient Agents with Model Fallback Strategies
- Understanding the AI Agent Threat Landscape and Defenses
- Implementing Layered Security for AI Agents
- Practical Demo: Defending Against Jailbreaking with Model Armor
07 Module 7: Capstone Projects: Building Real-World Agentic Systems
- Problem Definition and AI Agent Concepts
- Building and Testing a No-Code AI Agent in Zapier
- Project Introduction and Environment Setup
- Building the Planner Agent with Structured Outputs
- Implementing the Architect and Initial Coder Agents
- Finalizing the Coder Agent and Project Demonstration
- Fundamentals of Agentic AI and Your First Crew AI Agent
- Building Multi-Agent Crews with Custom and Pre-built Tools
- Production-Ready Crews with YAML Configuration
- Capstone Project: Building a Complete Marketing Crew
- Project: Build an IT Ticket Resolver with AutoGen and Azure AI
- Introduction to Legacy App Modernization with Velo AI
- Live Demo: Migrating an MS Access App to Blazor
- Code Deep Dive, The "Last Mile" Engineering, and Q&A
Real-world projects you'll work on
Practice the exact kind of work Agentic AI professionals do on the job.
Skills you'll gain
Add these to your LinkedIn profile and resume the day you finish.
Who should take this Agentic AI course?
And exactly what you need before starting (spoiler: almost nothing).
This course is for
- Software developers looking to transition into AI agent development and engineering.
- Data scientists wanting to expand their skill set into multi-agent systems and RAG pipelines.
- Tech professionals in India aiming to build production-ready AI applications for enterprise use.
- Computer science students seeking a practical agentic AI course free of charge to build their portfolios.
Prerequisites
- Basic understanding of Python programming and general software development principles is recommended.
- Familiarity with foundational machine learning concepts will help you grasp advanced topics faster.
- No prior experience with LangChain or CrewAI is required to start this agentic AI tutorial.
How learning works on LearnTube
The structure of a bootcamp, the price of free.
AI curates your path
Our AI picks the top 1% of video content for each topic and arranges it into a structured, goal-based curriculum. No more tutorial roulette.
Learn, then get quizzed
After every lesson, your AI tutor asks you a question and gives personalised feedback on your answer, so gaps get fixed immediately.
Finish with proof
Complete the course, build the capstone project, and claim a certificate recognised by 900+ hiring partners.
A complete Agentic AI course: free, structured and job-focused
Spanning about 20 hours and 86 video lessons, this agentic AI learning path takes you from basic concepts to advanced system deployment. You'll begin with the first lesson on Demystifying AI, Machine Learning, and Generative AI before moving into Module 1: Foundations of Agentic AI & LLMs. From there, the curriculum explores The Agentic Mindset: Advanced Prompting and Reasoning and provides A Developer's Guide to Agentic Frameworks. You will learn to use popular frameworks like LangChain, LangGraph, and CrewAI to build production-ready applications.
As you progress into the fourth module, you'll focus on giving agents tools, memory, and data. The course then covers Advanced Architectures: RAG and Multi-Agent Systems alongside Productionizing Agents: Deployment, Evaluation, and Security. Finally, Module 7 features Capstone Projects: Building Real-World Agentic Systems. Every topic is structured to help you understand state management, context engineering, and secure deployment practices.
LearnTube makes this process highly interactive. We curate the best YouTube video lessons and pair them with an AI tutor that tests your knowledge with a quiz after every single video. The entire learning experience is completely free. If you want to highlight your new skills to our 900+ hiring partners, you can opt for a paid certificate upon completion.
Jobs you can target after learning Agentic AI
Agentic AI is a gateway skill for some of the fastest-growing roles in tech.
Designs and deploys intelligent models and multi-agent systems for business applications.
Optimizes how large language models interpret instructions and execute complex reasoning tasks.
Builds the backend infrastructure and memory systems required for AI agents to function.
Typical entry-level salary ranges in India as of August 2026. Actual compensation varies by city, company and experience.
Earn a Agentic AI certificate employers recognise
Finish the course and claim a certificate you can share anywhere.
- Learn 100% free: every lesson, quiz and the AI tutor cost nothing
- Claim your certificate when you complete the course (an optional paid add-on)
- Recognised by 900+ hiring partners across India
- One click to share on LinkedIn and add to your resume
Why learners choose LearnTube
Real reviews from Google. You learn free, you get assessed free, and you pay only if you want the certificate.
Though I have 11 years of experience as an HRBP, the course helped me refresh my knowledge and gave me insights into new trends in the market. The modules were clear and easy to understand, and the quizzes were well designed. I have added the certificate to my LinkedIn profile.
I had a lot of doubts while learning, but the AI chatbot answered them in detail with simple summaries. The videos on the platform saved me a lot of time as I didn't have to search YouTube and watch random content.
Just earned my certificate in HR Management from LearnTube.ai! The platform is user-friendly and the assessment was a great way to validate my knowledge. Feeling proud of this accomplishment!
I enrolled in a LearnTube personalized course to get into a Data Analytics role. It helped me learn SQL in depth and gave me hands-on experience with Power BI. I attached the LearnTube certificate to my CV, it added credibility to my profile, and I'm currently in the interview process.
The interface is user-friendly and intuitive, making it easy to navigate the course. I encountered no errors during my time using the platform, which was a refreshing change from other online learning platforms.
Just cleared the Business Analyst assessment and got certified! Loved how the course simplifies big concepts like data-driven decision making into short, practical lessons. The automated quizzes kept me on track the whole time. Perfect boost for my resume!
The certificate gave me a real edge during my B.Tech. By the time I graduated, I had skills that set me apart from my peers, and the certification opened up more job opportunities. It gave me the confidence to stand out during interviews.
I manage many software projects as a General Manager and was looking for a good certification program to stay competitive. After researching many programs, I chose LearnTube and it was much better than I expected. The material is well-organized and practical, and what I liked most is the lifetime access.
Frequently asked questions
Everything learners ask before starting this Agentic AI course.
How long does this agentic AI course take to complete?
You will need about 20 hours to finish the entire program. The curriculum is spread across 86 video lessons, allowing you to learn at your own pace and easily fit the modules into your weekly schedule.
What will I be able to build after this course?
You'll be able to design and deploy complex multi-agent systems. Using frameworks like LangChain and CrewAI, you can create AI assistants with memory, state management, and RAG pipelines that solve real-world problems securely.
Is learning agentic AI worth it right now?
Yes, building AI agents is a highly sought-after skill in the tech industry. Companies are actively looking for developers who can move beyond basic chatbots to create autonomous systems that reason, use tools, and execute complex workflows.
Is this agentic AI certification course really free?
Yes, the entire learning experience is completely free. You can access all 86 video lessons and AI tutor quizzes without paying anything. The official certificate of completion is an optional paid add-on if you want it.
Can beginners take this agentic AI tutorial?
Absolutely. While some programming knowledge helps, the very first lesson focuses on demystifying AI, machine learning, and generative AI. The course gradually moves from basic foundational concepts to advanced deployment architectures over seven structured modules.
Are the classes self-paced or scheduled?
This program is entirely self-paced. You can watch the curated YouTube video lessons and complete the AI tutor quizzes whenever you have free time, making it easy to balance with work or university studies.
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