What Should a Skill Remember? Quality--Cost Trade-offs in Cost-Aware Skill Rewriting for Language Model Agents
arXiv:2606.09421v2 Announce Type: replace Abstract: Large language model agents increasingly rely on skills: reusable procedural documents encoding workflows, tool use, implementation patterns, validation checks, and domain rules. Skill rewriting is often treated as prompt compression, but shorter skills can make agents more expensive by removing sparse operational anchors that prevent exploration, debugging, and recovery. We study skill rewriting through this economic lens. Our controlled framework profiles skill structure, rewrites skills using information-preservation strategies, and evalua
The proliferation of large language model agents and their dependency on skills necessitates optimizing their performance and cost-efficiency through nuanced skill rewriting strategies.
Optimizing how AI agents use and rewrite 'skills' directly impacts their operational efficiency, capability, and economic viability, influencing the pace of AI deployment across industries.
The understanding of skill rewriting shifts from simple prompt compression to a complex trade-off between conciseness and operational robustness, guiding more sophisticated agent design paradigms.
- · AI agent developers
- · Cloud infrastructure providers
- · Businesses adopting AI agents
- · Inefficient AI agent models
- · Manual workflow optimization
- · High-cost exploratory AI operations
More cost-effective and reliable AI agents become accessible for a wider range of applications, driving adoption.
Reduced operational costs for AI agents could accelerate their integration into complex white-collar workflows, leading to increased automation.
The economic viability of agentic AI systems improves significantly, potentially impacting labor markets and requiring new frameworks for human-AI collaboration.
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