Agent swarms推动AI定价模式变革
Agent swarms正在改变AI定价逻辑，从固定费率转向按使用量计费，因为agent 24/7工作模式打破了传统基于人类提示速度的使用上限。
Agent swarms将大部分新token量分配给更便宜的模型，导致agent token量将超过前沿模型收入。在swarm架构中，parent agent负责规划，child agents处理搜索文件或运行测试等窄任务。开发者可将这些易验证的任务路由到能通过的最便宜模型，从而大幅降低成本。
Agent swarms send most new token volume to cheaper models, so agent token counts will outrun frontier-model revenue.
In a swarm, a parent agent plans while child agents handle narrow jobs like searching files or running tests. Narrow jobs are usually easy to check, so developers can route them to the cheapest model that passes.
And this massive token-explosion is also the major reason, flat-rate and per-seat pricing for AI software will give way to metering, because it relied on human prompting speed to cap usage. A person sends only so many prompts a day, so vendors could charge per user and still predict inference bills. But that equation will not hold with agent swarms working 24x7.
