This article discusses a study on AI agent systems, revealing that adding more agents can improve performance for certain tasks but can degrade it for others. It introduces a predictive model that helps identify the best architecture for various tasks based on their specific properties.

The article explains the limitations of AI swarms in producing coherent architecture due to their inherent properties of local optimization and lack of global coordination. It details how individual agents can generate working code but struggle to maintain consistency across architectural decisions. Ultimately, without a mechanism for enforcing global constraints, swarms will produce divergent outputs.