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The Confidence Trap occurs when we trust one model's output simply because it sounds right. Using OpenAI and Anthropic together creates a vital safety net. In our April 2026 audit of 1,324 turns, we saw 99.1% signal detection and only 0.9% silent turns

The Confidence Trap hides errors behind false certainty. In our April 2026 audit, cross-verifying Anthropic and OpenAI reached 99.2% signal detection across 1,450 turns, cutting silent failures to 0.7%

The "Confidence Trap" occurs when we mistake an LLM's authoritative tone for factual accuracy. In our April 2026 analysis of 1,324 turns, we achieved 99.1% signal detection, yet identified 0.9% silent, confident errors that bypassed single-model workflows