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arXiv cs.CL
arXiv cs.CL
7/1/2026
The original title is academic and long. I need to rewrite it as a punchy mobile feed headline, max 12 words, preserving key facts.

The original title is academic and long. I need to rewrite it as a punchy mobile feed headline, max 12 words, preserving key facts.

Original: A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization

Short summary

Enterprise AI agents misroute queries when skill descriptions overlap ('skill collision'). Researchers deployed an automated optimization pipeline on production systems (9 skills, 372 test cases) achieving 79.2% F1—matching manual tuning while reducing engineering time from 120 to 3.8 minutes per skill (32x faster). Ablation studies show a single LLM rewrite using error cases captures most of the improvement.

  • •Skill collision (overlapping descriptions) causes misrouting in scaled AI agent systems
  • •Automated optimization reduces tuning effort 32x (120→3.8 min/skill) while matching manual F1 performance
  • •Simple LLM rewrite with false-positive/negative cases drives most improvements; other optimizations matter less

Generated with AI, which can make mistakes.

#ai-agents#ai-tools#research-breakthrough#market-trend
Read full article at arXiv cs.CL

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