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

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

Instructor: Joe Moura

3,562 already enrolled

4 modules

Gain insight into a topic and learn the fundamentals.

11 reviews

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.

11 reviews

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

Category: Automation
Category: Context Management
Category: Agentic systems
Category: Memory Management
Category: Tool Calling
Category: Artificial Intelligence
Category: Artificial Intelligence and Machine Learning (AI/ML)
Category: Generative AI Agents
Category: Continuous Monitoring
Category: LLM Application
Category: Scalability
Category: Workflow Management
Category: Context Engineering
Category: Large Language Modeling
Category: System Monitoring

Tools you'll learn

Category: AI Workflows
Category: CrewAI
Category: AI Orchestration
Category: Model Context Protocol
Category: Agentic Workflows

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

4 assignments

Taught in English

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 videosTotal 58 minutes

Welcome7 minutes
Course Overview4 minutes
What Are AI Agents?6 minutes
Use Cases for AI Agents6 minutes
What Makes an AI Agent Intelligent?7 minutes
Planning Multi-Agent Systems4 minutes
Multi-Agent Systems in Production5 minutes
Tactics for Debugging, Observing, Optimizing7 minutes
Use Cases: Multi-Agent Systems at Scale5 minutes
The AI Agent Revolution and Why It's Happening Now7 minutes

3 readingsTotal 25 minutes

Course Syllabus10 minutes
Join the DeepLearning.AI Forum5 minutes
Module 1 Lecture Notes10 minutes

1 assignmentTotal 15 minutes

AI Agents and Applications15 minutes

1 programming assignmentTotal 180 minutes

Automatic Code Review180 minutes

2 ungraded labsTotal 120 minutes

Building your first AI Agent60 minutes
Building Multi-Agent Systems60 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 videosTotal 61 minutes

Understanding AI Agent Workflows6 minutes
Incorporating Memory and Knowledge7 minutes
Controlling Agents with Guardrails10 minutes
Controlling Agents with Execution Hooks5 minutes
Using Tools in Agents13 minutes
Adopting Model Context Protocol12 minutes
Building a Meeting Preparation Co-Pilot7 minutes

1 readingTotal 10 minutes

Module 2 Lecture Notes10 minutes

1 assignmentTotal 15 minutes

Decision Making, Tools and Model Context Protocol15 minutes

1 programming assignmentTotal 180 minutes

Adding Functionality to the Automatic Code Review180 minutes

2 ungraded labsTotal 120 minutes

Improving a Deep Research Crew60 minutes
Adding Tools to Your Deep Research Crew60 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 videosTotal 78 minutes

Collaboration7 minutes
Communication9 minutes
Using the A2A Protocol6 minutes
Orchestrating Agents with Flows21 minutes
Tactics for Building Reliable Systems15 minutes
Monitoring and Observability9 minutes
CI/CD for Agents12 minutes

1 readingTotal 10 minutes

Module 3 Lecture Notes10 minutes

1 assignmentTotal 15 minutes

Multi-Agent Systems, Safety & Reliability15 minutes

1 programming assignmentTotal 180 minutes

Creating a Flow for Automatic Code Review180 minutes

2 ungraded labsTotal 120 minutes

Building Coordination Patterns60 minutes
Building a Deep Research Flow60 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.

What's included

8 videos2 readings1 assignment

8 videosTotal 94 minutes

Applications Across Industries17 minutes
Finding Applications of AI Agents10 minutes
Applying AI Agents: Exa11 minutes
Applying AI Agents: Snyk15 minutes
Applying AI Agents: Weaviate13 minutes
Applying AI Agents: AB InBev15 minutes
The Future of AI Agents13 minutes
Conclusion1 minute

2 readingsTotal 20 minutes

Module 4 Lecture Notes10 minutes
Acknowledgments10 minutes

1 assignmentTotal 15 minutes

Agents across Application Domains and Enterprises 15 minutes

Instructor

Instructor ratings

(8 ratings)

Joe Moura

DeepLearning.AI

3 Courses34,301 learners

Offered by

DeepLearning.AI

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Reviewed on Dec 24, 2025

Extremely useful course to build agentic workflows.

Reviewed on Jan 14, 2026

Course is well structured and touch from simple to complex orchestration, using real case scenario for assignments.

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