arXiv:2611.11348v1 cs.AI

Separation of Skilling Context for Standard Operating Procedure-based Agents

Mitsuhiro Nakayama, Toshiaki Sota, Takehiko Yamaguchi, Michiaki Tatsubori

제출 2026-11-10

원문 초록

Standard Operating Procedures (SOPs) are kept concise for human readability, so LLM agents that generate executable skills from them lack the tacit knowledge and implementation details needed for automation. Human intervention fills these gaps, but must be repeated as SOPs evolve---a problem we call \textit{SOP-skill drift}. We propose managing SOPs and Contextual Knowledge (CK) separately, together with a \textit{contract-aware} evaluation that distinguishes genuine violations from CK-resolved differences. On eight Indeed-derived workflows (single LLM), it raises the functional-equivalence rate from 12\% to 58\%. Under three SOP-revision perturbations, previously extracted CK already covers 79.5\% of newly introduced automation gaps.

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