What if multi-agent AI systems fail less because the models are weak, and more because the agents are badly coordinated? This paper treats coordination as an architectural layer : who talks to whom, who decides, how outputs are merged, and how failures are handled.

The authors test five coordination patterns on prediction markets and find a sharp result for builders : more agents and more debate do not automatically create better systems. In this experiment, simple ensembles and sequential pipelines beat popular orchestration patterns on the cost-quality frontier.

Inspired by the work of Maksym Nechepurenko and Pavel Shuvalov, this episode was created using Google’s NotebookLM.

Read the original paper here :
https://arxiv.org/pdf/2605.03310