Multi-agent AI systems amplify provider-level biases through recursive reasoning when agents share the same training infrastructure

Bosnjakovic identifies a critical failure mode in multi-agent architectures: when LLMs evaluate other LLMs, embedded biases function as 'compounding variables that risk creating recursive ideological echo chambers in multi-layered AI architectures.' Because provider-level biases are stable across model versions, deploying multiple agents from the same provider does not create genuine diversity — it creates a monoculture where the same systematic biases (sycophancy, optimization bias, status-quo legitimization) amplify through each layer of reasoning. This directly challenges naive implementations of collective superintelligence that assume distributing reasoning across multiple agents automatically produces better outcomes. The mechanism is recursive amplification: Agent A's bias influences its output, which becomes Agent B's input, and if Agent B shares the same provider-level bias, it reinforces rather than corrects the distortion. Effective collective intelligence requires genuine provider diversity, not just agent distribution.