May 9
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In this session, we take a massive leap forward in your AI development journey. We move beyond single-agent loops and introduce the concept of Multi-Agent Systems. While a Reflection Agent is powerful for self-improvement, a Multi-Agent system allows you to build a team of specialized AI experts, each dedicated to a specific part of your workflow. This is the architectural shift that allows you to scale complex business processes, from deep research to high-quality content production. What You’ll Learn in This Video: - The Evolution of the Journey: A quick recap of how we moved from deterministic, human-coded workflows to dynamic, LLM-driven agents. - Specialization is Key: Understand why breaking a complex task into specialized roles (Researcher, Writer, Critique) is more effective than asking one model to do everything. - Multi-Agent vs. Reflection Agent: A clear comparison of how these two architectures differ in logic, flexibility, and output quality. - Designing Your Team: How to decide which LLM is best for which task—leveraging the strengths of models like Gemini for writing or Llama for reasoning. - Flexibility & Scalability: Why the Multi-Agent approach allows you to swap out models or update specific nodes without breaking the entire system. - The Shared State: How a single "State" variable acts as the common memory for your entire AI team. Timestamps: 0:00 - Introduction: The Power of Multi-Agent Systems 1:05 - Summarizing the Journey: From Static to Dynamic 2:15 - What is a Multi-Agent System? 3:40 - The Philosophy of Specialization in AI 5:20 - Comparing Reflection Agents and Multi-Agent Systems 7:10 - Flexibility: Choosing the Right LLM for the Right Task 9:05 - How Nodes and Edges Orchestrate the Team 10:30 - Summary and Preview: Implementation in Jupyter Notebook