Multi-Agent Systems in LangGraph/LangChain

Description

Multi-Agent Systems Guide: https://www.langcasts.com/products/digital_downloads/multi-agent-systems-guide Find me here: https://www.linkedin.com/in/fikayoadepoju/ In this foundational episode, we move beyond single-prompt engineering to explore the power of Multi-Agent Systems (MAS). As AI applications grow in complexity, a single "do-it-all" agent often becomes a bottleneck—suffering from context bloat, poor tool selection, and high latency. We’ll define exactly what a Multi-Agent System is and, more importantly, when it actually makes sense to use one. You’ll learn how to treat AI architecture like a high-functioning human team: dividing labor among specialists to achieve results that a generalist simply can't reach. What You Will Learn * The Definition of MAS: Understanding agents as autonomous, specialized entities that collaborate to solve complex, multi-step problems. * The "Why" Behind Multi-Agent: * Benefits: Increased accuracy through specialization, improved fault tolerance (if one agent fails, the system stays up), and better resource management. * Tradeoffs: Navigating the increased complexity, higher token costs, and communication latency that come with coordination. * Single vs. Multi-Agent: A decision framework for knowing when to stick with a simple agent and when to scale to a team. * Real-World Use Cases: Exploring MAS in software engineering (the "Developer Team" pattern), customer support, and market research. The 4 Core Multi-Agent Systems We provide a brief but essential introduction to the four patterns that govern how agents interact: 1. Sub-Agents: The "Supervisor" pattern where a main agent calls specialized workers as tools to handle sub-tasks in isolation. 2. Handoffs: The "Relay Race" pattern where control of the conversation is fully transferred from one specialist to another (ideal for sequential workflows like Support $\to$ Billing). 3. Router: The "Switchboard" pattern where a classifier directs queries to the best-fit agent in parallel, synthesizing their results at the end. 4. Skills: The "Persona-on-Demand" pattern where a single agent dynamically loads specific instructions or "skills" based on the task at hand. By the end of this video, you’ll have the architectural mental model needed to design AI systems that are modular, scalable, and truly professional. 🔥 Ready to stop building "monoliths" and start building "teams"? Let’s dive into Multi-Agent Systems! 👍 If you find these architectural deep-dives valuable, please Like and Subscribe to support the channel! 👇 Which of the 4 patterns (Sub-Agents, Handoffs, Router, or Skills) sounds most useful for your current project? Let's discuss in the comments! #MultiAgentSystems #AIAgents #LangGraph #LangChain #AIArchitecture #SoftwareEngineering #AgenticAI #MachineLearning #TechTutorial #Routing #SubAgents #Handoffs #AIScaling