⚡Intro to Multi-Agent Fundamentals Using CrewAI

Learning Outcomes
1. Understand agents, the brains behind the operation Learn to define a CrewAI agent using role, goal, backstory, LLM, and tools so it knows what job it owns.
2. Write tasks that tell agents what to deliver Learn to build a task with description, agent, expected_output, and context so the result is predictable.
3. Assemble a crew that runs agents and tasks Learn to use the crew as the container that brings your agents and tasks together and actually runs them.
4. Run crews sequentially, concurrently, or hierarchically Learn to pick the right processing mode and see how design patterns map to how your crew executes work.
5. Build a working multi-agent crew end to end Learn to connect multiple agents, tasks, and a crew into one run where agents hand off work to each other.
Why This Topic Matters
One agent hits a ceiling fast. Real work has steps, handoffs, and specialists, and a single prompt cannot carry all of it. CrewAI gives you agents, tasks, and a crew that runs them in sequential, concurrent, or hierarchical order. You leave able to break a messy process into roles a system can run, which is how AI moves from answering questions to doing the job.