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Building Multimodal Multi-Agent Systems with CrewAI

  • The course "Multi-Agent System" focuses on the study of systems where multiple intelligent agents interact, collaborate, or compete to achieve goals. It covers key concepts such as agent communication, coordination, negotiation, learning, and decision-making. The course explores real-world applications in designing and implementing multi-agent solutions for complex problems.
₹4,245.00
  • Level
    Intermediate
  • Duration
    4 hours
  • Last Updated
    April 22, 2025
  • Certificate
    Certificate of completion

Material Includes

  • Video Lessons - Intuition and Implementation
  • Hands-On Guide for Practice
  • Downloadable Learning Resources

What I will learn?

  • Understanding Multi-Agent Systems – Gain a solid foundation in the concepts, characteristics, and types of agents, including their interactions and decision-making processes.
  • Agent Communication & Coordination – Learn how agents collaborate, negotiate, and coordinate tasks efficiently in a distributed environment.
  • Practical Implementation with CrewAI – Develop hands-on experience in designing, deploying, and optimizing multi-agent systems using AI frameworks like CrewAI.
  • Building Efficient Systems – Understand how to enhance multi-agent system performance through role specialisation, tool integration, and memory optimization.

Course Curriculum

Module 1: Understanding AI Agents and CrewAI

  • Getting Started with AI Agents: A Beginner’s Guide
    13:42
  • Key Concepts and Functions of AI Agents
  • CrewAI Explained: Introduction of CrewAI
    04:53
  • How CrewAI Works: A Deep Dive
    08:00
  • Understanding CrewAI with a Use Case
  • Creating Agent : Agent for writing a research article
    14:41

Module 2: Deep Dive into Key Element of Agents

Module 3: CrewAI Tools and Their Components for Building Agentic AI

Module 4: Multi-Agent Collaboration and Task Execution

Module 5: Intelligent Multi-Agent System

Module 6 : Multimodal Multi Agent System and their Issues in AI Agents

Self-Assessment

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