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About this paper
Xingyan Liu, Xiyue Luo, Linyu Li, Ganghong Huang, Jianfeng Liu, Honglin Qiao
SkillForge introduces a framework for generating and continuously refining domain-specific skills for LLM agents in cloud technical support, leveraging enterprise data for initial creation and an automated loop for self-evolution. The framework improved agent performance by +13.76 percentage points in Strict Consistency Rate compared to a production legacy system.
Key insights
- Grounding initial agent skill creation in specific domain knowledge and historical operational data substantially improves initial skill quality and alignment with enterprise requirements.
- An automated, continuous self-evolution loop effectively diagnoses and refines agent skills from various starting points, including human-authored ones, leading to monotonic performance gains over iterations.
- While effective, the automated refinement of knowledge-related failures can plateau due to factors like reference inconsistencies, tacit expertise, and design constraints, suggesting limits to purely automated, text-based optimization.
Summary by alphaXiv.
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