What you will learn

  • Learn the building blocks to build effective AI agents

  • Apply best practices to build complex agentic workflows

  • Build advanced multi-agent systems in minutes!

Course curriculum

    1. Course Introduction

    2. Course Objectives

    3. Course Tips

    1. Introduction to AI Agents

    2. AI Agent Components

    3. Why AI Agents

    1. Introduction to Flowise AI

    2. Flowise AI Installation Notes

    3. Getting Started with Flowise AI

    4. Flowise AI Example

    1. Introduction to Agentic Workflows

    2. Agent Components

    3. ReAct Agent

    1. Build Your First Agent

    2. Important Update for Flowise Agentflow

    3. Web Scraping Agent

    1. Introduction to Multi-Agent Systems

    2. Build a Multi-Agent System

About this course

  • 20 lessons
  • 1.5 hours of video content
  • Projects to apply learnings
  • Earn a Certificate of Completion
  • Beginner

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Instructor(s)

Elvis Saravia, Ph.D.

Founder and Lead Instructor

Elvis is a co-founder of DAIR.AI, where he leads all AI research, education, and engineering efforts. Elvis holds a Ph.D. in computer science, specializing in NLP and language models. His primary interests are training and evaluating large language models and developing scalable applications with LLMs. He co-created the Galactica LLM at Meta AI and supported and advised world-class teams like FAIR, PyTorch, and Papers with Code. Prior to this, he was an education architect at Elastic where he developed technical curriculum and courses on solutions such as Elasticsearch, Kibana, and Logstash.

COURSE OVERVIEW

This course focuses on building effective and complex AI agents. Students will learn the fundamental building blocks for creating AI agents and the best practices for constructing advanced agent-based workflows. The course also covers advanced concepts such as building multi-agent and hierarchical multi-agent systems. 

After completing the course, students will have a solid understanding of AI agents and how to develop an effective framework for building advanced agentic AI systems for different domains and problems. 

PREREQUISITES

This course doesn't have any prerequisites and doesn't require programming. If you are not familiar with prompting techniques for LLMs, we strongly recommend completing both the Introduction to Prompt Engineering and Advanced Prompt Engineering courses (also available to all Pro members).

The main tool you will use is Flowise AI (a no-code tool to build chat flows and agentic workflows), therefore, no programming is required. More details and instructions about how to access and install Flowise AI are provided in the course.

TOPICS

Throughout the course, students will utilize Flowise AI, a no-code tool that simplifies the process of developing complex agent-based workflows. 

Key concepts covered in the course include:

  • AI Agent Definitions: AI agents are LLM-powered systems capable of performing tasks on a user's behalf. They excel at resolving broad and complex problems by leveraging planning, reflection, tool access, and memory.
  • Agent Components: AI agents consist of tools, memory, and planning components to effectively execute tasks. They are especially valuable for solving complex tasks that involve multiple steps, looping, or numerous iterations. Students will learn about the different components and how to build effectively with them. 
  • ReAct Agent: An important concept for building powerful AI agents is known as ReAct. Students will learn the key ideas behind ReAct and how to build a simple ReAct agent. 
  • Agentic Workflows: Agentic workflows employ AI agents to automate complex tasks such as scientific discovery, research, coding, marketing, content design, and planning. The LLM serves as the agent's central operator, referred to as the agent's "brain." 
  • Flowise AI Agentic workflows: Flowise AI, a no-code tool, offers a sophisticated framework for building advanced agent-based workflows. It integrates features from LangChain and LlamaIndex, enabling seamless prototyping and module iteration. Students will use Flowise AI to build their first search agent that can retrieve real-time information from the web.
  • Web Scraping Agent: Involves an example of an agent that leverages information scraped from the web using a retrieval tool. 
  • Multi-Agent Systems: Involving different specialized agents to tackle various tasks for a comprehensive solution. The course demonstrates this with a marketing copywriter agentic system. 
  • Hierarchical Agents: Involves supervisor/worker agents who communicate and collaborate with each other to perform complex and advanced tasks. The course demonstrates this with a course planner agent.


Here's a subset of companies whose employees have benefited from our courses:

Accrete, Airbnb, Alston & Bird, Amazon, Apple, Arm, Asana, Bank of America, Belong For Me, Biogen, Brilliant, Carebound, CDM, CentralReach, Centric Software, Chime, Coinbase, Digital Green, DoHQ, Elekta, Fidelity Investments, Fivecast, Fulcrum Labs, Google, Guru, Gretel, Harrison Insights, Intel, Intuit, Jina AI, JPMorgan Chase & Co, Khan Academy, KnowBe4, Lawyer.com, LinkedIn, LionSentry, MagmaLabs, MasterClass, Meta, Metopio, Microsoft, Moneta Health, Oracle, OpenAI, Rechat AI, RingCentral, Salesforce, Scale AI, Scribd, Space-O Technologies, Sun Life, TD Bank, TELUS Corporations, Trilogy, TTEC Digital, UniCredit, VaxCalc Labs, Vendr, Walmart, Wolfram Alpha, Zemoso Technologies, Zeplin 

Reach out to [email protected] with any questions and for team/student discounts.

Join Pro to Get Started

Get unlimited access to all our AI courses, special tutorials, certificates, live webinars, workshops, office hours, dedicated support, and community discussions.

What people are saying

“This course gave me valuable insights into routing and parallelization of LLMs, which were key for building efficient AI agents. I also found the concept of an evaluator-optimizer agent particularly interesting for improving application performance.”

Rahul Jain | Co-Founder & CEO at Pixeldust Technologies

“Taking the "Introduction to AI Agents" course was a transformative experience. The course broke down complex concepts into practical building blocks, which made it easy to understand how to construct effective AI agents. What stood out most was the clarity of the instructions and how it was done step by step. The learning from each module built on the previous one. The interactive demonstrations were especially valuable, allowing me to apply best practices in real time and reinforce my learning through hands-on tasks. By the end, I was able to confidently design advanced multi-agent systems and workflows. I highly recommend this course to anyone looking for a practical, engaging, and well-structured introduction to building AI agents.”

Tom Themeles

“Introduction to AI Agents is a great foundational course. The content is relevant and well presented, I could see how I could apply some of the concepts immediately in my own work. Looking forward to a deep dive with the Advanced AI Agents course next!”

Nick Taylor

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