Practical Multi AI Agents and Advanced Use Cases with crewAI

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.

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