The Interaction Tax: When Communication Erases Diversity in Multi-Agent Teams
Why talking too much makes AI teams less creative
When multiple AI models share their complete solutions with each other, they quickly converge on the same answer within a single round, eliminating the diversity that made having multiple models useful in the first place. Across 11 optimization tasks with equal budgets, having agents work independently produced better results than letting them see each other's full outputs, because interaction caused them to stick with the first solution they encountered rather than exploring different approaches.
As companies build larger AI systems by combining multiple models, this work reveals a hidden cost: unrestricted communication between agents can actually make the system worse, not better. The practical implication is straightforward—teams need to control what information agents share and when, rather than assuming more interaction always helps. This could reshape how companies design multi-agent systems, shifting focus from how many models they use to what those models are allowed to tell each other.