Originally published on AI Tech Connect.
What actually drives multi-agent token spend The multiplication is structural, not incidental. Every subagent call typically re-sends the system prompt, tool schemas and relevant context from scratch — the same instructions get billed N times, not once, before the model has done any new work. Aggregate spend hides the problem. A monthly API bill tells you the total went up; it doesn't tell you which agent, which tool-call retry loop, or which redundant file read caused it. You need per-agent accounting to fix anything. The fix is rarely "use a cheaper model everywhere." The highest-leverage changes are structural — caching shared prefixes, capping fan-out, deduplicating tool calls — before you touch model choice at all. Anthropic's own published account of building a multi-agent research…
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