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Learner Reviews & Feedback for Multi AI Agent Systems with crewAI by DeepLearning.AI

4.8
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308 ratings

About the Course

Learn key principles of designing effective AI agents, and organizing a team of AI agents to perform complex, multi-step tasks. Apply these concepts to automate 6 common business processes. Learn from João Moura, founder and CEO of crewAI, and explore key components of multi-agent systems: 1. Role-playing: Assign specialized roles to agents 2. Memory: Provide agents with short-term, long-term, and shared memory 3. Tools: Assign pre-built and custom tools to each agent (e.g. for web search) 4. Focus: Break down the tasks, goals, and tools and assign to multiple AI agents for better performance 5. Guardrails: Effectively handle errors, hallucinations, and infinite loops 6. Cooperation: Perform tasks in series, in parallel, and hierarchically Throughout the course, you’ll work with crewAI, an open source library designed for building multi-agent systems. You’ll learn to build agent crews that execute common business processes, such as: 1. Tailor resumes and interview prep for job applications 2. Research, write and edit technical articles 3. Automate customer support inquiries 4. Conduct customer outreach campaigns 5. Plan and execute events 6. Perform financial analysis By the end of the course, you will have designed several multi-agent systems to assist you in common business processes, and also studied the key principles of AI agent systems....

Top reviews

JC

Dec 24, 2024

Fantastic course to understand how to build agentic workflows

AH

Jul 11, 2024

Highly recommended, a great practical approach to this fundamental field in GenAI.

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51 - 52 of 52 Reviews for Multi AI Agent Systems with crewAI

By Lech M

Nov 23, 2025

1. I assume this training is for at least semi-technical people, showing how to install python and that to create a file you need to click icon is stating the obvious 2. Enable line wrap when showing code 4. Imho agents without LLMs are not like team that cannot communicate. Imho LLMs are not for communication, please update "analogies". I think it would be close to team without brains ;-) 5. Rethink how you show the code. We're adding lines without description what they do, then a video later we say oh, now we need to add search tool, but ou, we have it, and we have that too and we have that. So maybe it's better to add search tool only on that later video when we talk about it? 6. I can bet that not everyone has OpenAI and Serper accounts, you may want to make a remark about signing in OR creating 7. Have the content reviewed by someone next time, please 8. Another weird thing - first we add process = Process.sequential, in the code and only in the next video we talk about different process types. Sequence seems to be super random in this training. 9. We built code and now we're back to theory without running it? After creating code I would like to run it vs listening to another piece of theory. We describe tools AFTER we created one. 10. It's not easy to understand the speaker sometimes, the voice is going up and down, please use text to speech engine for the voice over 11. I think the sample assumes some OpenAI account with $$, I get error "You exceeded your current quota, please check your plan and billing details" - that's not a good experience. 12. Clearly state what will be the $ cost of running those examples 13. Output from the sample is providing information from 2023 as cutting edge.. that's a bit crappy example imho 14. Why do we repeat content between tasks? we talk about top 3 AI articles both to researcher and writer, shouldnt writer write blog based on the content that researcher provides? I think we're making writer too specific to what we know that researcher provides, while we should make it generic enough to handle other researchers... 15. Link Agent Swarms, Implementing Open Source LLMs with Crew AI points to generic https://docs.crewai.com/#connect-crewai-to-llms (I think the anchor is gone)

By Srinivas S

Sep 13, 2025

course doesn't load properly