Multi-Agent AI Systems Add Coordination Overhead Before Delivering Real Gains

Expanding an AI system from one agent to multiple agents immediately introduces coordination costs — such as task assignment, context preservation, and result reconciliation — before any capability improvement is realized. The article argues that a second agent only justifies its added complexity when it addresses a clearly named capability gap that the single-agent baseline cannot reliably handle. Developers are advised to establish a competent single-agent baseline first, then split responsibilities only when the delegated task has a defined boundary and a verifiable output. The piece introduces a coordination-budget framework to help teams weigh the recurring overhead of each new agent role against its concrete benefit. Drawing on a design principle from Anthropic's December 2024 guidance, the author emphasizes that more messages or tool calls do not constitute improvement — only reliably completed, user-valued outcomes do.