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One agent doing everything sounds fine ā until it doesn't. In this video we take the same Tokyo population task from Videos 3, 4, and 5 and split it across two specialised agents: a Researcher who can only search and store, and a Calculator who can only retrieve and compute. Neither can do the other's job. The role boundary is enforced at the tool level ā not just in the prompt. This is CrewAI. And it's the last piece of the Orchestration layer in this LLMOps series. š What's covered: ā Why one generalist agent breaks down in production ā CrewAI's three primitives: Agent, Task, Crew ā Role design ā how backstory and goal actually shape behaviour ā Tool scoping ā giving each agent only what their role requires ā The context parameter ā one line that handles the entire agent handoff ā Process.sequential vs Process.hierarchical ā when to use each ā Reading the live output ā clean execution vs Video 5's recovery behaviour ā The built-in CrewAI trace link ā free observability out of the box Full LLMOps Series ā Orchestration Layer: Video 3 ā Raw ReAct Agent (pure Python, 200 lines) Video 4 ā LangChain AgentExecutor (66 lines) Video 5 ā LangGraph (you own the graph) Video 6 ā CrewAI Multi-Agent ā you are here #CrewAI #AIAgents #LLMOps #MultiAgent #Python #LangChain #LangGraph #GenerativeAI #MachineLearning #SoftwareEngineering