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New agentic short course! Multi AI Agent Systems with crewAI, built with CrewAI's founder and CEO João Moura. In this course, you'll learn how to break down complex tasks into subtasks for multiple AI agents, each playing a specialized role, to execute. For example, to generate a research report,... you might have researcher, writer, and quality assurance agents collaborate. You'll define the roles, expectations, and interactions between the agents—like a manager organizing a team. You'll work with key agentic AI techniques like role-playing, tool use, memory, guardrails, and cross-agent collaboration. And you'll build your own multi-agent systems that can tackle complex tasks. I think you'll find it both productive and fun to design agents and watch them collaborate to get things done. Let me know what you think! I believe multi-agent architectures will drive significant progress in AI systems. Please sign up here!show more

Andrew Ng

1,586,068 subscribers

349,839 views • 2 years ago •via X (Twitter)

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10 Comments

Amanda2 years ago

@crewAIInc @joaomdmoura This is great, Andrew. Absolutely agree. Especially the QA agents.

Jesse2 years ago

@crewAIInc @joaomdmoura Excited to see this new course on multi-agent AI systems from @crewAIInc! Breaking down complex tasks for specialized agents is a powerful approach. I've used crewAI for my own agentic workflows and have been impressed. Looking forward to checking out the full course.

blank2 years ago

@crewAIInc @joaomdmoura 👀 study time.

Arihant Parsoya2 years ago

@crewAIInc @joaomdmoura This is an innovative approach to leverage AI capabilities. Organizing multiple agents with specialized roles mimics real world collaboration and has great potential to efficiently solve complex problems.

John Xie2 years ago

@crewAIInc @joaomdmoura Multi-Agent collaboration is the way. Completely agree and we're also betting on it.

Nique Fajors2 years ago

@crewAIInc @joaomdmoura Just signed up.

Felipe Jordão A.P Mattosinho2 years ago

@crewAIInc @joaomdmoura That's so cool! Congrats @joaomdmoura

AIxBlock2 years ago

@crewAIInc @joaomdmoura Thanks for sharing this opportunity! At AIxBlock, we’re keen on exploring innovative AI techniques like these. Delegating specialized tasks to different AI agents and seeing them collaborate seamlessly is a fascinating approach!

Dankoyy2 years ago

@crewAIInc @joaomdmoura Brabissimo! Fantástico João!! 🤩🤩

xl2 years ago

@crewAIInc @joaomdmoura Leeets gooo @joaomdmoura

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Erika Shorten

61,317 views • 1 year ago

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

New short course: Building Code Agents with Hugging Face smolagents! Learn how to build code agents in this course, created in collaboration with Hugging Face, and taught by Thomas Wolf, its co-founder and CSO, and m_ric, Hugging Face’s Project Lead on Agents. Tool-calling agents use LLMs to generate multiple function calls sequentially to complete a complex sequence of tasks. They generate one function call, execute it, observe, reason, and decide what to do next. Code agents take a different approach. They consolidate all these calls into a single block of code, letting the LLM lay out an entire action plan at once, which can be executed efficiently to provide more reliable results. You’ll learn how to code agents using smolagents, a lightweight agentic framework from Hugging Face. Along the way, you’ll learn how to run LLM-generated code safely and develop an evaluation system to optimize your code agent for production. In detail, you’ll learn: - How agentic systems have evolved, gaining greater levels of agency over time—and why code agents are a next step. - How code agents write their actions in code. - When code agents outperform function-calling agents. - How to run code agents safely in your system using a constrained Python interpreter and sandboxing using E2B. - To trace, debug, and assess the code agent to optimize its behaviours for complex requests. - How to build a research multi-agent system that can find information online and organize it into an interactive report. By the end of this course, you’ll know how to build and run code agents using smolagents, and deploy them safely with a structured evaluation system in your projects. Please sign up here!

Andrew Ng

124,382 views • 1 year ago