Google's 180-configuration study shows multi-agent AI fails on sequential tasks
Google Research tested 180 agent configurations across five architectures and found multi-agent systems boost parallel tasks by 81% but degrade sequential work by up to 70%. The predictive model correctly identifies optimal setups for 87% of unseen tasks—challenging the 'more agents always better' assumption driving enterprise AI deployments.