signalAI热榜2026-09-24
Team Shares Prompt to Improve Agent Harness Token Efficiency
This article provides a detailed prompt and methodology for reducing token costs in LLM agent frameworks. Key results include a 7% overall cost reduction with no quality loss, shrinking system prompts by two-thirds, cutting tool description tokens by 60%, and reducing cold cache misses by 20% through explicit breakpoints. It covers measuring baselines, optimizing prompts, tool definitions, cache layout, tool outputs, compression, and model mixing.
- for who
- Developers and teams building or optimizing LLM agent frameworks
- why now
- Token cost savings are available now from this shared agent harness optimization prompt.
- what changes
- They can systematically measure and reduce token costs across the harness without sacrificing task quality
- to do
- Follow the stepped approach: baseline measurement, prioritize opportunities, implement safe changes, and report results
key points
- 7% total cost reduction with no quality loss from prompt, tool, cache changes
- Explicit cache breakpoints and settings after them cut cold misses by 20%
#token efficiency#agent framework#prompt optimization#cost optimization#cache layout
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