Free Agentic AI Course with Certification 2026

About Course

New to AI agents? You're in the right place this course starts from the ground up.

Everyone is building AI agents; almost no one is building agents that remember. By default, the large language model at the centre of any agent is stateless it starts every call cold, forgets what you told it yesterday, and quietly falls apart the moment a task spans more than one session.

This free Agentic AI course is built around that single, under-appreciated problem: AI agent memory. It's the difference between an agent that demos well and one that survives production.

You'll start by seeing an agent forget in real time, and by understanding why bigger context windows are a workaround, not a memory system.

From there, the course walks the full memory pipeline the way a working engineer would build it so you learn how to build AI agents that hold onto context: the anatomy of agent memory, turning noisy conversation into structured knowledge through ingestion and extraction, retrieving the right memory at the right moment, and settling once and for all how AI memory differs from RAG.

Then it goes where pure vector search can't: knowledge graphs, entity resolution, and time. It closes with the things teams learn the hard way scoping and privacy across multiple tenants, and memory hygiene, forgetting, and honest evaluation.

This is a hands-on, code-first AI agents course closer to a guided agentic AI tutorial than a lecture series. Across roughly 1 hour 50 minutes of video in 7 modules, with companion Colab notebooks built on LangGraph and Chroma, you won't just watch you'll build a broken agent, feel exactly where it breaks, and fix it piece by piece.

It begins beginner-friendly and steadily ramps to real, intermediate-level engineering, so you finish able to design a memory layer for an agent you actually care about, evaluate whether it works using benchmarks like LongMemEval and LoCoMo, and make a clear-eyed build-vs-adopt decision.

Some Python and basic familiarity with LLMs and APIs will help you get the most out of it. There's no faster way to learn agentic AI than building the memory layer yourself.

As for your course instructor, Gaurav Dadhich will be the one supporting you through your learning journey with his pre-recorded lectures. He is the Founder of Maximem AI, and brings hands-on, production experience of building agentic AI systems straight into every module so you learn to build agents that remember from someone who does it for real.

Because the course is self-paced, you can learn whenever it fits your schedule and receive the Scaler Certificate of Excellence an agentic AI certification you can add to your resume and LinkedIn after completing the modules and hands-on notebooks.

Pre-requisites for this free Agentic AI online course with certificate

This free Agentic AI course welcomes newcomers to the world of AI agents you can start with curiosity and no prior agentic-AI background, and the early modules build the core ideas from scratch.

To get the most from the hands-on parts, some comfort with Python and basic familiarity with LLMs and APIs (prompts, tokens, and calling a model) will help, because later modules use LangGraph, embeddings, and vector databases.

You do not need deep machine-learning maths the course is engineering-focused, not research-focused. To follow along you'll need a computer with internet access, a Google account for the companion Colab notebooks, and an LLM API key (free tiers work). The course walks through the setup as you go.

Who should learn this Agentic AI course?

Beginners curious about AI agents who want a structured, hands-on path from "what is an agent?" to building a real agent-memory pipeline. Developers and engineers already building AI agents who've hit the "memory wall" agents that forget context across sessions and fall apart in production.

Product engineers, ML/AI practitioners, and builders who need reliable memory and want to understand RAG vs memory and when to use each. Anyone who wants a free, certificate-backed way to move from beginner agentic-AI concepts to intermediate and advanced agent engineering.