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arXiv · 2608.01772

FRAMES: Guarded and Dual-Objective Skill Evolution for Agents in Policy-Governed Enterprise Workflows

Xuhui Wang·Ruoqi Shu·Chen Dan·Tianhua Xu·Mengxi Luo·Yanming Mai·Bo Wan
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Abstract

LLM agents increasingly run policy-bound enterprise workflows such as document auditing, where they must apply rules consistently, ground every value, and stay auditable. Improving these agents is hard: operational feedback is sparse and unlabeled, edits to one rule can regress unrelated cases, and accuracy must improve without inflating inference cost or losing auditability. We present FRAMES, a closed-loop framework that cold-starts deployable skills from existing assets and then evolves them through consensus-based mutation, Pareto selection over accuracy and cost, and an anti-regression guarantee, all while preserving auditability. Deployed on our internal production system, FRAMES attains the best accuracy-cost trade-off among baselines, with the same gains reproduced on tau-bench.

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Xuhui Wang, Ruoqi Shu, Chen Dan, Tianhua Xu, Mengxi Luo, Yanming Mai, Bo Wan. 2026-08-03. FRAMES: Guarded and Dual-Objective Skill Evolution for Agents in Policy-Governed Enterprise Workflows. https://arxiv.org/abs/2608.01772

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