When splitting AI agents into multiple subagents helps, and when it only adds cost

Dividing one AI agent into several is a common design choice that is often more expensive than it is worth. The main benefit is that each subagent works in its own clean context window, returning only a short summary, which helps with broad tasks that would overflow a single context. Independent subtasks can also run in parallel. The costs include substantially higher token usage, with one cited multi-agent research system using about 15 times the tokens of a plain chat, plus compounding errors across handoffs, loss of context, and harder debugging. The article recommends building and measuring a single well-equipped agent first, splitting only when context overflow or clearly parallel work justifies it, and always comparing against the simpler version.