The convergence problem also seems to require an explicit definition of what “agreement” means. Two agents can produce different outputs that are both internally valid, while two seemingly similar outputs can hide a meaningful disagreement in assumptions or evidence. I’d make the merge layer responsible for identifying those differences rather than forcing everything into a single answer. That could mean comparing structured claims, source provenance, confidence, and unresolved assumptions before aggregation. The important part is that convergence shouldn’t always end in consensus—sometimes the correct output is a clearly surfaced conflict that gives the human enough context to decide. That makes the convergence layer a decision-support boundary rather than just another processing step.
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The convergence problem also seems to require an explicit definition of what “agreement” means. Two agents can produce different outputs that are both internally valid, while two seemingly similar outputs can hide a meaningful disagreement in assumptions or evidence. I’d make the merge layer responsible for identifying those differences rather than forcing everything into a single answer. That could mean comparing structured claims, source provenance, confidence, and unresolved assumptions before aggregation. The important part is that convergence shouldn’t always end in consensus—sometimes the correct output is a clearly surfaced conflict that gives the human enough context to decide. That makes the convergence layer a decision-support boundary rather than just another processing step.