GoKawiilGoogle Research study finds adding AI agents helps parallel tasks, hurts sequential ones
A blog post from InsForge argues that large-scale multi-agent AI systems should be treated as distributed systems, citing a Google Research study of 180 configurations. That study found adding agents improved parallel work by up to 80.9% but degraded sequential work by 39% to 70%, and an orchestrator reduced error amplification from 17.2x to 4.4x. A separate benchmark called Silo-Bench (ACL 2026) tested teams of 2 to 100 agents and found the hardest tasks dropped to zero success once teams reached 50 agents.