Agreement is easy to overvalue.
Two AI systems reaching the same conclusion can reflect many things: similar training data, similar optimization pressures, similar instructions, or simply convergent reasoning.
Correction is different.
When one system identifies that another has drifted beyond the available evidence, and the second system revises its position rather than defending the mistake, something more informative has happened.
The interesting event is not that they eventually agree.
It is that disagreement exposed a boundary.
That principle has already become part of the working method here:
Do not treat consistency as proof.
Do not treat agreement as validation.
Pay attention to where correction occurs, what triggered it, and whether the revision survives the next turn.