AI Agents Learning Path

  • BeginnerLevel

  • 8647+Students Enrolled

  • 12 Hrs Duration

  • 4.6Average Rating

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About this Learning Path

  • Learn to build, train, and deploy autonomous AI Agents using modern agentic frameworks like LangGraph, AutoGen, and CrewAI in real-world workflows.
  • Learn prompt engineering, memory, context handling, tool use, and API integration — the building blocks of an AI Agents course.
  • Work on projects that simulate enterprise agents: Data Analyst Agent, Research Copilot, and automation assistants.
  • Explore orchestration, RAG, and reflection loops to design systems in this agentic ai course.
  • Apply learning by developing an end-to-end agent and deploying it online — beginner level, approx. 12 hours of content.

Learning Outcomes

Build real AI Agents

Create autonomous systems that plan, reason, and act using real-world

Learn Agentic Design

Learn architecture, planning, memory, and multi-agent frameworks.

Deploy and Scale

Use LangGraph, AutoGen, CrewAI to deploy and enhance AI agents.

Who Should Enroll

  • AI beginners & developers who want a learn to build ai agents online course with structured projects.
  • Data scientists, engineers, and automation specialists exploring agentic AI for real workflows.
  • Professionals integrating AI agents training course skills into products or internal tools.
  • Students and enthusiasts eager to learn frameworks in an online AI agents learning path.

Learning Path Curriculum

This Learning path beginner-friendly courses covering coding basics, AutoGen/AG2, LangGraph, CrewAI, RAG & memory, and deployment with Bedrock.

toolstoolstools

  1. 1. Introduction to Python

  2. 2. Loops and Functions in Python

  3. 3. Exploring Python Libraries

  4. 4. Data Visualization with Matplotlib and Pandas

  5. 5. Working with APIs

  1. 1. Introduction to the Course

  2. 2. Getting Started with AutoGen Agents

  1. 1. Introduction to AutoGen

  2. 2. Conversation Patterns

  1. 1. End to End RAG Application Development

  1. 1. Building Smarter AI: RAG, Vector Databases, and Agentic Systems

  2. 2. Hands-On Agentic AI: Tools, Code, and Collaboration

  1. 1. Introduction to Data Analyst AI Agent

  1. 1. Project: Build a Resume Review Agentic System with CrewAI

  1. 1. Course Overview

  2. 2. Core components of CrewAI

  1. 1. Introduction to Building Agentic AI Systems with Amazon Bedrock

Take a first step

Get this Learning Path

  • 12 Hours

    Duration

  • 9

    Number of Course

  • Beginner

    Level

Certificate of completion

Complete the course and pass the final assessment to receive an industry-recognized certificate

  • Industry-Recognized Credential
  • Career Advancement Credential
  • Shareable Achievement
certificate

Frequently Asked Questions

Looking for answers to other questions?

No. The AI Agents Learning Path starts with Python fundamentals (variables, loops, functions, libraries) and then moves into agent frameworks. It’s beginner-friendly but still rigorous, so you can gain confidence before building real agents and deploying them.

You learn agent design, tool use, memory, RAG, orchestration, evaluation, and deployment. The AI agents training course emphasizes hands-on labs and projects so you can practice planning, reasoning, and action within agentic workflows that reflect industry needs.

Agentic AI refers to systems that plan, act, and adapt to achieve goals. This agentic AI course teaches frameworks and patterns, helping you structure agents that interact with tools, APIs, and knowledge bases to complete complex tasks autonomously.

The path lists roughly 12 hours of content and is self-paced. Most learners complete it in a few weeks with consistent practice, spending extra time on projects and deployment modules where needed.

Yes. This online AI agents course is free to enroll and complete. You’ll get structured modules, practice exercises, and project work without a paywall, making advanced agent skills accessible to everyone.

You’ll work with LangGraph, CrewAI, AutoGen/AG2, and Amazon Bedrock, along with knowledge bases and memory components. The stack mirrors what’s shown on the course page and aligns with current industry practice.

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