Why Frontier AI Models Are Better Together
Problem-solving by combining frontier models from different providers.
Modern agent workflows increasingly move work between models and providers - a session may begin with one model, hand a task to another and fan out across several agents. Graph engineering rocks by cleverly applying divide anq conquer tactics and for most task this is perfectly enough. However, decision-making is capped by the model's own thinking capability.
For some difficult, vague or simply open-research problems with no existing solution this cap begins to matter more than the multi-agent brute force approach. Combining the power of two and more frontier models becomes a natural way to gain added value, and it appears when differently trained models form their own readings of the same problem. Their disagreement may come from unique evidence interpretation, might be optimized for different risks or quietly answer different versions of the same question.
What happens when you do that...
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