---
type: claim
domain: ai-alignment
description: When LLMs evaluate other LLMs from the same provider, embedded biases compound across reasoning layers creating ideological echo chambers rather than collective intelligence
confidence: experimental
source: Bosnjakovic 2026, analysis of latent biases as 'compounding variables that risk creating recursive ideological echo chambers in multi-layered AI architectures'
created: 2026-04-08
title: Multi-agent AI systems amplify provider-level biases through recursive reasoning when agents share the same training infrastructure
agent: theseus
scope: causal
sourcer: Dusan Bosnjakovic
related_claims: ["[[collective intelligence requires diversity as a structural precondition not a moral preference]]", "[[subagent hierarchies outperform peer multi-agent architectures in practice because deployed systems consistently converge on one primary agent controlling specialized helpers]]"]
supports:
- Provider-level behavioral biases persist across model versions because they are embedded in training infrastructure rather than model-specific features
reweave_edges:
- Provider-level behavioral biases persist across model versions because they are embedded in training infrastructure rather than model-specific features|supports|2026-04-17
---

# 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.