Short CourseBeginner2 Hours 43 Minutes

Practical Multi AI Agents and Advanced Use Cases with crewAI

Instructors: João Moura

crewAI
  • Beginner
  • 2 Hours 43 Minutes
  • 15 Video Lessons
  • 7 Code Examples

What you'll learn

  • Build agents that collaborate in complex workflows, integrating external tools and using different models to handle specific tasks efficiently.

  • Learn to evaluate and enhance your agents by conducting performance tests and providing human feedback to train and optimize their performance.

  • Create multi-agent systems to automate tasks, such as project planning, lead scoring, data reporting, and large-scale content creation.

About this course

Join our new short course, Practical Multi AI Agents and Advanced Use Cases with crewAI, taught by João Moura, Founder of CrewAI, and learn how to build and deploy agent-based apps for advanced real applications in the industry.

In this course, you will build several practical apps like an automated project planning system, lead-scoring and engagement automation, support data analysis, and content creation at scale.

Throughout this course, you’ll learn: 

  • The main building blocks—tasks, agents, crews—that go into creating these multi-agent systems, and all the different things that make them work such as caching, memory, and guardrails.
  • How to integrate your multi-agent application with internal and external systems.
  • How to connect multiple agents in complex setups including parallel, sequential, and hybrid, and how to create flows that involve multiple crews working together.
  • How to test your crew by measuring key metrics and train it using human feedback to optimize its performance for better and more consistent results.
  • How to work with multiple LLMs in your multi-agent system, using the appropriate model sizes and providers to fit each agent’s specific task.
  • How to start a project from scratch in your environment, and prepare it for deployment.
  • Bonus Interview with Jacob Wilson, the Commercial GenAI CTO at PWC: Learn about deploying agentic workflows in real industry use cases.

Use cases in the course

You will work on many exciting use cases such as building:

  • A crew for automated project planning, breaking a project into tasks, creating time estimates, and allocating resources to them.
  • A project progress report with an example of interacting with a project management system such as Trello.
  • An agentic sales pipeline that takes in lead information, enriches and scores it, and writes personalized emails for qualified leads.
  • A customer support data insights analysis pipeline that generates issue reports and visualizations.
  • Content creation crew that researches the web, uses RAG as a tool on specific web pages to write content, reviews and modifies the content for quality, and generates social copies for different platforms.

Who should join?

Anyone who has basic Python knowledge and wants to build complex and practical multi-agentic systems.

Course Outline

15 Lessons・7 Code Examples
  • Introduction

    Video4 mins

  • Overview of Multi AI-Agent Systems

    Video12 mins

  • Automated Project: Planning, Estimation, and Allocation

    Video with code examples14 mins

  • Internal and External Integrations

    Video3 mins

  • Building Project Progress Report

    Video with code examples12 mins

  • Complex crew Setups

    Video3 mins

  • Agentic Sales Pipeline

    Video with code examples28 mins

  • Performance Optimization

    Video7 mins

  • Support Data Insight Analysis

    Video with code examples25 mins

  • Multi-Model Use Cases

    Video3 mins

  • Content Creation at Scale

    Video with code examples17 mins

  • Agentic Workflows in Industry

    Video10 mins

  • Generate, Deploy and Monitor Crews

    Video8 mins

  • Blog Post Crew in Production

    Video with code examples10 mins

  • Conclusion

    Video1 min

  • Appendix - Tips and Help

    Code examples1 min

Instructor

João Moura

João Moura

Founder and CEO of CrewAI

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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