Split a complex AI task across multiple specialized agents instead of forcing one agent to do everything.
A multi-agent system uses multiple agents that have different responsibilities. For example, one agent might research information, another might analyze it, and a third might create the final response.
Multiple agents can make complex workflows easier to organize, but they also introduce communication and coordination problems. More agents do not automatically mean a better system, so the architecture should use multiple agents only when specialization or parallel work provides a real benefit.
What you'll walk away knowing
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