A single agent can do a lot, but it has limits: one context window, one set of instructions, and one chain of reasoning to keep on track. A multi-agent system splits the work across several agents, each with its own role, tools, and focus, and then coordinates them so they solve the problem together.
This module starts with two short explainers on why teams of agents are worth building and what they cost you, including how having agents check one another can reduce hallucinations. Then you'll see the main coordination patterns in practice. In LangGraph, a supervisor agent routes tasks to specialists through handoffs, and supervisors can be stacked into hierarchies of teams. In CrewAI, you define role-based agents, give them tasks and tools, and run them as a crew.
As you watch, ask what each extra agent actually buys you. Multi-agent setups add coordination overhead, more model calls, and new ways to fail, so they pay off mainly when work can be parallelized or divided into clearly different specialties. Many problems are still best solved by one well-designed agent.
A concise introduction to what a multi-agent system is: how LLM-powered agents are organized, how they cooperate, and why scaling out to several agents can beat relying on just one.
Video 8minLooks at the reliability side of multi-agent design: how agents that verify one another can catch confident-but-wrong answers, and where this matters most in high-stakes settings.
Video 11minThe LangGraph Supervisor pattern in action: a supervisor orchestrates specialized agents through tool-based handoffs, then you see how to nest supervisors into hierarchical teams.
Video 12minA beginner-friendly crash course on role-based multi-agent crews in CrewAI: build agents, tools, and crews (including YAML configuration), then finish with a complete marketing-crew project.
Video 1h 11min