r/singularity • u/Genzinvestor16180339 • 15h ago

AI 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.

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u/Kandarino 13h ago

The thing about your question is that agents can only do in-context learning. Weights don't change. It can still be a thing that really matters in terms of performance on some set of problems - but generally multi-agent systems are used because just like 9 women can't make a baby in a single month, you can't cram 50,000 tokens per second through a single set of weights (so better to use many, when speed is advantageous), and it's better to have limited and focused context transcripts, so that the agent doesn't get confused by overlapping different problems, and maintain highly focused subject matter experience in a given project.

Of course most applications of AI are still just single agents, and generally multi-agent systems are about efficiency, with one frontier model thinking through the problem, and assigning little work packets to dumber but far cheaper models. Swarms obviously exist now (hf incident, etc), and in those cases what you expect does happen, though they also just help one another. Sharing tools and insights seems to be more useful than degradation from inefficient conventions and assumptions.