Could multi-agent AI create a “groupthink” problem that prevents exceptional individual agents from emerging?

A lot of multi-agent AI seems structurally similar to agent-based models: many individual agents interact, share information, and produce a collective outcome. But if those agents are constantly learning from or correcting one another, could that create herding, correlated errors, and convergence toward the same way of thinking? Would there be value in training some agents much more independently, with their own memory, feedback, and decision history, so they can develop different strategies? In that world, could one individual agent eventually become dramatically better than the rest—the equivalent of an “Einstein” agent? I am not understanding why we have thousands of agents interacting versus building one super smart one that is making decision's on its own.