Design, Develop, and Deploy Multi-Agent Systems with CrewAI

Design, Develop, and Deploy Multi-Agent Systems with CrewAI

Instructor: Joe Moura

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2,732 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Beginner level

Recommended experience

2 weeks to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

Build practical multi-agent systems that collaborate, use tools and memory, and scale reliably to production.
Use tools such as web search and MCP servers to extend your agents’ real-world capabilities.
Add guardrails, hooks, and low level control with CrewAI Flows to make AI systems safer, more predictable, and production-ready.

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

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Assessments

4 assignments

Taught in English

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There are 4 modules in this course

Multi-agent systems let generative AI go beyond single tasks, enabling teams of agents that can plan, reason, and collaborate to solve complex problems.

In this course, you’ll learn how to build multi-agent systems that automate complex, end-to-end workflows. You’ll create intelligent agent teams that plan, reason, and collaborate using tools, memory, and guardrails, and learn how to scale them for production. Across four modules, you’ll build practical applications including an automated code reviewer, a meeting co-pilot, and a deep researcher, each showcasing real-world design patterns for agent collaboration. You’ll monitor and debug agent performance using traces, evaluate behavior with LLM-as-a-Judge, and apply best practices for continuous improvement in production. By the end, you’ll be able to implement custom multi-agent systems that perform reliably, deliver measurable outcomes, and scale to thousands of users.

In this module, you will design and develop single- and multi-agent systems from concept to prototype. You'll tune agent behavior using context engineering, study real-world use cases, and examine how these systems run in production. You will complete the graded quiz and Automatic Code Review graded programming assignment to pass the module.

What's included

10 videos3 readings1 assignment1 programming assignment2 ungraded labs

10 videos Total 58 minutes

Welcome 7 minutes
Course Overview 4 minutes
What Are AI Agents? 6 minutes
Use Cases for AI Agents 6 minutes
What Makes an AI Agent Intelligent? 7 minutes
Planning Multi-Agent Systems 4 minutes
Multi-Agent Systems in Production 5 minutes
Tactics for Debugging, Observing, Optimizing 7 minutes
Use Cases: Multi-Agent Systems at Scale 5 minutes
The AI Agent Revolution and Why It's Happening Now 7 minutes

3 readings Total 25 minutes

Course Syllabus 10 minutes
Join the DeepLearning.AI Forum 5 minutes
Module 1 Lecture Notes 10 minutes

1 assignment Total 15 minutes

AI Agents and Applications 15 minutes

1 programming assignment Total 180 minutes

Automatic Code Review 180 minutes

2 ungraded labs Total 120 minutes

Building your first AI Agent 60 minutes
Building Multi-Agent Systems 60 minutes

In this module, you'll learn to control agent behavior with guardrails, execution hooks, memory, and knowledge to guide richer decision cycles. You will build and integrate custom tools, and learn how the Model Context Protocol is expanding agent capabilities. You will complete the graded quiz and the Automatic Code Review graded programming assignment to pass the module.

What's included

7 videos1 reading1 assignment1 programming assignment2 ungraded labs

7 videos Total 61 minutes

Understanding AI Agent Workflows 6 minutes
Incorporating Memory and Knowledge 7 minutes
Controlling Agents with Guardrails 10 minutes
Controlling Agents with Execution Hooks 5 minutes
Using Tools in Agents 13 minutes
Adopting Model Context Protocol 12 minutes
Building a Meeting Preparation Co-Pilot 7 minutes

1 reading Total 10 minutes

Module 2 Lecture Notes 10 minutes

1 assignment Total 15 minutes

Decision Making, Tools and Model Context Protocol 15 minutes

1 programming assignment Total 180 minutes

Adding Functionality to the Automatic Code Review 180 minutes

2 ungraded labs Total 120 minutes

Improving a Deep Research Crew 60 minutes
Adding Tools to Your Deep Research Crew 60 minutes

In this module, you will orchestrate agents in complex coordination pattern using sequential, parallel, hierarchical, hybrid, and async processes. You'll also implement Flows as a low-level control layer for orchestration. Finally, you'll learn how to monitor multi-agent systems with tracing, sampling, and observability tools, as well as train agents using human-in-the-loop feedback and structured evaluations. You will complete the graded quiz and Automatic Code Review Flow graded programming assignment to pass the module.

What's included

7 videos1 reading1 assignment1 programming assignment2 ungraded labs

7 videos Total 78 minutes

Collaboration 7 minutes
Communication 9 minutes
Using the A2A Protocol 6 minutes
Orchestrating Agents with Flows 21 minutes
Tactics for Building Reliable Systems 15 minutes
Monitoring and Observability 9 minutes
CI/CD for Agents 12 minutes

1 reading Total 10 minutes

Module 3 Lecture Notes 10 minutes

1 assignment Total 15 minutes

Multi-Agent Systems, Safety & Reliability 15 minutes

1 programming assignment Total 180 minutes

Creating a Flow for Automatic Code Review 180 minutes

2 ungraded labs Total 120 minutes

Building Coordination Patterns 60 minutes
Building a Deep Research Flow 60 minutes

In this module, you will explore how businesses adopt agents across industries and functions, from early chatbots to workflow co-pilots. You will analyze real deployments through case study interviews featuring leaders from Exa, Snyk, Weaviate, and AB InBev. You will complete the graded quiz to pass the module.