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This article explores whether multi-agent AI systems could lead to groupthink, where agents converge in thinking and hinder the emergence of exceptional individual agents, questioning the trade-off between many interacting agents versus a single super-intelligent agent.
The author recounts an experiment where using the same AI model as both code writer and reviewer, even with different prompts, failed to catch expensive correlated errors like assuming an endpoint is idempotent, and found that real independence requires external verification from production traces or assertions.