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A public prompt cut Agent Harness token cost by 7% with no quality loss
1 report1 sourceupdated 6 days ago
What happened
Summary
一个团队分享了一条公开提示词，用来优化 LLM Agent 框架（就是让模型在业务流程里干活的工具），通过精简提示词、把部分工具逻辑挪到框架外、调整缓存布局、稀疏行号、调子智能体等操作，把每个任务的 token 成本压低了约 7%，而且任务质量没下降。提示词还建议按任务而不是按请求来计量成本，先画框架图、测基线，再按优先级改。正文没披露用了什么模型、跑...
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Sep 24
- AI HOT (Curated Pool)A public prompt cut Agent Harness token cost by 7% with no quality loss
A team shared a public prompt to optimize LLM Agent Harness, cutting per-task token cost by ~7% without quality loss through prompt trimming, tool offloading, cache layout, sparse line numbers, and sub-agent tuning. The prompt advises metering by task, not by request, and mapping the harness, measuring baseline, then prioritizing changes. The post doesn't disclose the model used, task set, or baseline token cost.