A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization
A single LLM rewrite using false-positive and false-negative cases can match manually tuned skill descriptions, reducing engineering effort by 32x while maintaining routing accuracy.
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[Submitted on 29 Jun 2026]
Title:A Single Rewrite Suffices: Empirical Lessons from Production Skill Description Optimization
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Abstract:Enterprise AI agents route user queries to specialized skills by matching queries against natural language skill descriptions. When two skills share overlapping descriptions, the routing LLM misroutes queries, a failure we term skill collision. As agents scale to dozens of skills, manually tuning descriptions to maintain routing accuracy becomes a significant engineering bottleneck. We deploy an automated description optimization pipeline on a production enterprise group chat agent (9 skills, 372 regression cases). The pipeline produces descriptions averaging 79.2% F1, matching manually tuned descriptions at 79.4% F1 (average per-skill difference -0.20%, within the 0.78% multi-seed noise floor), while reducing per-skill engineering effort from 120 minutes to 3.8 minutes (32 times speedup). We then examine which pipeline components actually drive this match. Systematic ablation on both the production system and ToolBench (16k tools) reveals that a single LLM rewrite using any available false-positive and false-negative cases captures most of the available improvement. Other design choices we tested (iteration budget, feedback signal composition, dual editing of confused pairs, and training set size) each affect final F1 by less than 0.5%. Description optimization addresses skill collisions caused by overlapping descriptions but cannot resolve cases where two skills intended scopes genuinely overlap. We identify a diagnostic (a large train-validation F1 gap) that flags the latter cases for architectural rather than text-level intervention.
Comments: 12 pages, 4 figures
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.30775 [cs.CL]
(or arXiv:2606.30775v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.30775
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yangqiaoyu Zhou [view email] [v1] Mon, 29 Jun 2026 18:06:43 UTC (164 KB)
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