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AI Agent Design Patterns with CrewAI
Rating: 4.5 out of 5(471 ratings)
2,498 students

AI Agent Design Patterns with CrewAI

Master CrewAI's agent-based automation for real-world AI workflows.
Created byTensor Teach
Last updated 8/2025
English
English [Auto],Spanish [Auto],

What you'll learn

  • Understand the fundamentals of CrewAI, including Agents and Tasks.
  • Understand the fundamentals of building AI Agents.
  • Integrate planning, reflection, and human input into CrewAI workflows.
  • Develop multi-agent collaboration strategies for real-world applications.

Course content

4 sections • 22 lectures • 1h 31m total length

  • Overview of CrewAI2:47

    Explore Crew AI, a modular Python framework for building multi-agent systems, focusing on agents, tasks, and the crew to introduce core agent design patterns and fundamentals.

  • Note from Instructor About Next Video0:08
  • Agents & Tasks in CrewAI8:04

    Define ai agents as autonomous systems that iteratively take actions based on environment state to achieve an objective, performing tasks, using tools, and collaborating in crew ai.

  • Building Our First Crew7:04

    Build a simple crew with one customer support agent and one support task using crew AI, including defining the role, goal, backstory, and running the crew to completion.

  • Defining Agents & Tasks Using YAML5:57

    learn how to define agents and tasks using YAML configuration in CrewAI, replacing in-code definitions with separate agents.yaml and task.yaml files for a cleaner, scalable setup and easier query handling.

  • Agents Executing Multiple Tasks4:44

    Guide an ai agent to execute multiple tasks by decomposing a support request into return, shipping, and complaint policies, then generate a formal email referencing all outputs using memory.

  • Resources for Section 10:02

Requirements

  • Basic familiarity with Python is recommended
  • No prior experience with CrewAI is needed; everything will be taught from scratch.

Description

This course, "AI Agent Design Patterns with CrewAI," is designed to provide a comprehensive hands-on guide to working with CrewAI and mastering AI-driven automation.

In this course, you will start with the fundamentals of CrewAI, learning about agents and tasks and how to define them using YAML configurations. You will then explore tool usage, equipping your agents with powerful functionalities such as web search and context retrieval.

Next, we delve into planning, reflection, and human input, showing you how to build AI agents that can strategize, adapt, and incorporate human-in-the-loop decision-making. Finally, you will learn how to design multi-agent collaboration, enabling agents to work together effectively in various use cases, including customer support automation.

By the end of this course, you will have:

  • A strong understanding of CrewAI's core components

  • The ability to design, configure, and deploy AI agents

  • Knowledge of advanced AI agent capabilities such as planning and reflection

  • Practical skills in multi-agent collaboration and real-world AI automation

This course is perfect for developers, data scientists, AI enthusiasts, and business professionals looking to automate workflows with AI agents. No prior experience with CrewAI is required—just a basic understanding of Python and a willingness to explore AI automation.

Enroll now and start building the future of AI-driven automation!

Who this course is for:

  • Developers, data scientists, and AI enthusiasts interested in multi-agent AI systems.
  • Business professionals looking to automate workflows using AI agents.
  • Researchers exploring agent-based decision-making and collaboration.
  • Anyone curious about CrewAI and how it can be used for real-world problem-solving.

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