SkillForge: Forging Domain-Specific, Self-Evolving Agent Skills in Cloud Technical Support

View PDF HTML (experimental) Abstract:Deploying LLM-powered agents in enterprise scenarios such as cloud technical support demands high-quality, domain-specific skills. However, existing skill creators lack domain grounding, producing skills poorly aligned with real-world task requirements. Moreover, once deployed, there is no systematic mechanism to trace execution failures back to skill deficiencies and drive targeted refinements, leaving skill quality stagnant despite accumulating operational evidence. We introduce SkillForge, a self-evolving framework that closes an end-to-end creation-evaluation-refinement loop. To produce well-aligned initial skills, a Domain-Contextualized Skill Creator grounds skill synthesis in knowledge bases and historical support tickets. To enable continuous self-optimization, a three-stage pipeline -- Failure Analyzer, Skill Diagnostician, and Skill Optimizer -- automatically diagnoses execution failures in batch, pinpoints the underlying skill deficiencies, and rewrites the skill to eliminate them. This cycle runs iteratively, allowing skills to self-improve with every round of deployment feedback. Evaluated on five real-world cloud support scenarios spanning 1,883 tickets and 3,737 tasks, experiments show that: (1) the Domain-Contextualized Skill Creator produces substantially better initial skills than the generic skill creator, as measured by consistency with expert-authored reference responses from historical tickets; and (2) the self-evolution loop progressively improves skill quality from diverse starting points (including expert-authored, domain-created, and generic skills) across successive rounds, demonstrating that automated evolution can surpass manually curated expert knowledge. Comments: Accepted at ACM SIGIR 2026 Industry Track. 18 pages, 5 figures, 3 tables Subjects: Information Retrieval (cs.IR); Artificial Intelligence (cs.AI); Software Engineering (cs.SE) ACM classes: H.3.3; I.2.7 Cite as: arXiv:2604.08618 [cs.IR] (or arXiv:2604.08618v1 [cs.IR] for this version) https://doi.org/10.48550/arXiv.2604.08618 arXiv-issued DOI via DataCite Submission history From: Xingyan Liu [view email] [v1] Thu, 9 Apr 2026 07:11:23 UTC (55 KB)