Design, Develop, and Deploy Multi-Agent Systems with CrewAI
by DeepLearning.AI and CrewAI
A four-module CrewAI course that takes one code-review agent from prototype to a monitored, CI/CD-deployed multi-agent system.
Overview
Design, Develop, and Deploy Multi-Agent Systems with CrewAI is DeepLearning.AI's longer CrewAI course, separate from the older one-hour short course on the same framework. It is taught by João Moura, founder and CEO of CrewAI. The DeepLearning.AI page lists it as beginner level, last updated July 17, 2026, with 13 hours 8 minutes of material: 38 video lessons, 6 code examples and 7 graded assignments that require a Pro membership ($25/month billed annually or $30 monthly, checked November 12, 2026). It also runs on Coursera, where it was announced in November 2025 and shows a 4.5 rating from 13 reviews and 4,579 enrolled learners. There are four modules. Foundations of AI Agents covers use cases, planning a multi-agent system, production concerns and debugging. Working with AI Agents covers memory, guardrails, execution hooks, tools, MCP and a no-code agent, and has you build a deep research crew. Managing Systems of AI Agents covers coordination patterns, the A2A protocol, Flows, reliability, observability and CI/CD. Applying AI Agents in Business discusses prioritizing use cases, with conversations featuring Exa, Snyk, Weaviate and AB InBev. The three programming assignments build on one project, an automatic code reviewer, which is extended with tools and then rebuilt as a Flow. Coursera budgets about 180 minutes for each of those assignments, so the real time cost is closer to 20 hours than the 13 hours of video.
At a Glance
- Topic
- Agentic
- Level
- Beginner
- Format
- Course
- Cost
- Paid
- Duration
- ~13 hours of video; Coursera estimates ~2 weeks at 10 hours/week including labs
- Provider
- DeepLearning.AI and CrewAI
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Building single CrewAI agents with memory, tools, guardrails and execution hooks
- ✓Connecting agents to external tools through Model Context Protocol (MCP) servers
- ✓Orchestrating multi-agent workflows with both CrewAI Crews and event-driven Flows
- ✓Applying coordination patterns and the A2A protocol for agent-to-agent communication
- ✓Adding observability with traces, LLM-as-a-judge testing and human feedback loops
- ✓Deploying and monitoring agents in production with versioning and CI/CD pipelines
- ✓Prioritizing which business use cases are worth automating with multi-agent systems
Highlights
- •Taught by João Moura, who created the CrewAI framework
- •One project (an automatic code reviewer) carries through three graded programming assignments, so the work compounds
- •Covers the production side most agent intros skip: tracing, evaluation, versioning and CI/CD
- •Coursera lists 4.5 stars from 13 reviews and 4,579 learners (checked 2026-11-12); budget about 20 hours, not the 13 of video
Who It’s For
Best For
- ✓Python developers who want a structured path from first agent to deployed multi-agent system
- ✓Teams that have standardized on CrewAI and want the framework's author explaining Crews vs. Flows
- ✓Technical leads evaluating how to monitor and ship agent workflows in production
Prerequisites
- •Python programming experience
- •Basic familiarity with calling an LLM API; no prior agent framework experience needed
FAQ
What is Design, Develop, and Deploy Multi-Agent Systems with CrewAI?
A full-length DeepLearning.AI course taught by CrewAI founder João Moura for Python developers moving into agentic AI. Across four modules you build agents with memory, tools, MCP servers and guardrails, orchestrate them with Crews and Flows, add tracing and LLM-as-a-judge evaluation, and deploy with versioning and CI/CD.
Is Design, Develop, and Deploy Multi-Agent Systems with CrewAI free?
Design, Develop, and Deploy Multi-Agent Systems with CrewAI is a paid resource.
What level is Design, Develop, and Deploy Multi-Agent Systems with CrewAI for?
Design, Develop, and Deploy Multi-Agent Systems with CrewAI is aimed at a beginner audience. Recommended background: Python programming experience, Basic familiarity with calling an LLM API; no prior agent framework experience needed.
How long does Design, Develop, and Deploy Multi-Agent Systems with CrewAI take?
Expect roughly ~13 hours of video; Coursera estimates ~2 weeks at 10 hours/week including labs. Most learners work through it at their own pace.
What will I learn from Design, Develop, and Deploy Multi-Agent Systems with CrewAI?
You'll learn: Building single CrewAI agents with memory, tools, guardrails and execution hooks; Connecting agents to external tools through Model Context Protocol (MCP) servers; Orchestrating multi-agent workflows with both CrewAI Crews and event-driven Flows; Applying coordination patterns and the A2A protocol for agent-to-agent communication; Adding observability with traces, LLM-as-a-judge testing and human feedback loops; Deploying and monitoring agents in production with versioning and CI/CD pipelines; Prioritizing which business use cases are worth automating with multi-agent systems.
Topics
Sources
This page was written from 3 sources, 2 on domains other than deeplearning.ai.