Introduction to AI Agents
Learn how to build agentic workflows powered by LLMs.
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 Introduction
Course Objectives
Course Tips
Introduction to AI Agents
AI Agent Components
Why AI Agents
Introduction to Flowise AI
Flowise AI Installation Notes
Getting Started with Flowise AI
Flowise AI Example
Introduction to Agentic Workflows
Agent Components
ReAct Agent
Build Your First Agent
Important Update for Flowise Agentflow
Web Scraping Agent
Introduction to Multi-Agent Systems
Build a Multi-Agent System
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:
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.
“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.”
“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.”
“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!”
Learn important prompt engineering techniques to build use cases with LLMs.
Learn the most important prompting techniques and best practices for building effective LLM applications.
Learn how to build effective and modern Retrieval Augmented Generation (RAG) systems.
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