AgenticFrameworks

Multi AI Agent Systems with crewAI

by DeepLearning.AI × crewAI

IntermediateCourseFree~2-3 hours

Orchestrate teams of role-playing agents that collaborate on multi-step business tasks with crewAI.

Start LearningReviewed July 3, 2026

Overview

Taught by crewAI founder João Moura, this course shows how to break a complex task across multiple specialized agents that delegate to and critique one another. You build crews for research, customer support, and content workflows, giving each agent a role, goal, backstory, and toolset. The course emphasizes practical design patterns for reliable multi-agent collaboration rather than toy demos.

At a Glance

Topic
Agentic
Level
Intermediate
Format
Course
Cost
Free
Duration
~2-3 hours
Provider
DeepLearning.AI × crewAI
Hands-on
Yes — code/exercises
Certificate
None

What You’ll Learn

  • Designing agents with roles, goals, and tools
  • Task delegation and collaboration between agents
  • Wiring agents to real tools and data
  • Multi-agent patterns for research and support automation

Highlights

  • Taught by crewAI's creator
  • Focus on multi-agent collaboration, not single agents
  • Reusable crew templates you can adapt

Who It’s For

Best For

  • Developers automating multi-step business workflows

Prerequisites

  • Intermediate Python

FAQ

What is Multi AI Agent Systems with crewAI?

Learn to design multi-agent 'crews' — agents with roles, goals, and tools that cooperate to automate real workflows.

Is Multi AI Agent Systems with crewAI free?

Multi AI Agent Systems with crewAI is free to access.

What level is Multi AI Agent Systems with crewAI for?

Multi AI Agent Systems with crewAI is aimed at a intermediate audience. Recommended background: Intermediate Python.

How long does Multi AI Agent Systems with crewAI take?

Expect roughly ~2-3 hours. Most learners work through it at their own pace.

What will I learn from Multi AI Agent Systems with crewAI?

You'll learn: Designing agents with roles, goals, and tools; Task delegation and collaboration between agents; Wiring agents to real tools and data; Multi-agent patterns for research and support automation.

Topics

crewAImulti-agentorchestrationautomation