Design, Develop, and Deploy Multi-Agent Systems with CrewAI
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.
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IN COLLABORATION WITH
Your path to production-ready agents
- Build agentic systems that work together: Create multi-agent workflows where agents plan, reason, and collaborate to complete complex tasks reliably, including with tool use and MCP servers
- Control and improve your agents: Use memory, guardrails, execution hooks, traces, and low-level control layers to ensure reliable, repeatable outcomes.
- Deploy with confidence: Orchestrate agents with two common paradigms – Crews and Flows – that allow you to scale systems from prototype to production.
Why Enroll
AI agents leverage the power of Large Language Models (LLMs), but, as with all LLM-based tools, they struggle with reliability, coordination, and repeatability when deployed on complex workflows. AI agents build on these models to move from responding to prompts to acting autonomously, reasoning through tasks, and adapting to changing goals. Multi-agent systems extend this capability even further by distributing reasoning and responsibilities across specialized agents that can plan, collaborate, and improve together.
While it’s never been faster to prototype a concept, many teams are still stuck at this prototype stage, where agents might run well at a small scale but fail under real-world conditions. In this course, you’ll bridge that gap by turning prototypes like an automated code reviewer, a meeting co-pilot, and a deep researcher into production-ready systems. You’ll use the CrewAI framework to apply methods that improve control, reliability, and scalability.
Across four modules, you’ll:
- Build AI agents using core the building blocks of memory, tools (including MCP servers), guardrails, and execution hooks.
- Design and orchestrate multi-agent workflows using Flows and complex coordination strategies. In hands-on labs, create and refine crews for projects such as a deep researcher and a meeting co-pilot.
- Add observability and evaluation through traces, testing with LLM-as-a-Judge techniques, and training with human feedback to monitor agent decisions, debug issues, and continuously improve performance.
- Deploy and monitor agents safely in production, integrating zoom-in and zoom-out observability metrics, versioning your configurations, and scaling reliably with production-grade practices.
By the end, you’ll know how to turn your agent ideas into scalable systems that are robust, observable, and ready for real-world use.
In partnership with
We built this course with the CrewAI team to share the framework and techniques powering many of today’s most advanced agentic systems. You’ll learn directly from João Moura, Co-founder and CEO of CrewAI, through hands-on labs that guide you from building single agents to deploying multi-agent systems ready for production.
Who should join?
This course is designed for AI builders and technical professionals who want to understand, build, and scale AI agent systems, from engineers and developers to students and technical leaders guiding AI adoption. Whether you’re hands-on with code or leading development teams, you’ll gain the knowledge to design multi-agent workflows, integrate them into real applications, and make informed decisions about deploying them safely and reliably.
Instructor
João Moura
João Moura is the Co-founder and CEOof CrewAI, an open-source framework for building and orchestrating multi-agent systems. With nearly 20 years of experience in software and AI engineering, he has led distributed teams and driven large-scale innovation at companies like Clearbit (acquired by HubSpot) and Toptal. João is an international conference speaker and passionate technologist known for helping organizations design reliable, production-ready AI systems that bridge the gap between research and real-world applications.
Syllabus
You’ll earn a certificate upon completing the course, recognizing your skills in designing, developing, and deploying multi-agent systems!
What Learners From Previous Courses Say About DeepLearning.AI
“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
Frequently Asked Questions
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