Why multi-agent AI costs more: what the extra budget buys

多 agent 不是更聪明，是更贵：它到底买到了什么

Multi-agent setups spend more tokens to gain specific engineering benefits, and whether a task gains depends on its parallelism, how much information it must cover, and its context state. Drawing on Anthropic, Cognition and several studies, the article sorts the gains into four kinds: shorter end-to-end time, gap-filling, more search diversity and context isolation. Team cost is usually several times that of a single agent.

Why it matters: It breaks multi-agent selection into task bottlenecks and payoff conditions, matching independent subtasks, information coverage and context pollution to different delegation and isolation choices.