THE CLAIM. parallel subagents are the next leap for AI agents. On August 13 Claude Code turned subagent forking on by default, OpenAI's Sol Ultra fans a task across up to 64 concurrent subagents, and the industry heuristic driving all of it is, in the words of the researchers who tested it, the belief that adding specialized agents will consistently improve results.

THE CHECK. Google Research and academic collaborators ran the first controlled study large enough to answer it, 260 configurations across six agentic benchmarks and five architectures, holding tools, prompts, and compute fixed. The verdict is not that multi-agent is fake. It is that task structure decides. On a parallelizable benchmark like Finance-Agent, coordination delivered up to +81%. On a strictly sequential one like PlanCraft, every multi-agent variant they tested degraded performance by 39 to 70%. Their own summary: coordination improves performance on parallelizable tasks but degrades it on sequential ones. The open question for the two coding agents pushing subagents hardest is which pattern their default triggers: fanning out to gather context in parallel is the winning case, but splitting a dependent job like debugging or planning across coordinating agents is the losing one, and much of what developers hand these tools is the dependent, tool-heavy kind.