FRAMES: Guarded and Dual-Objective Skill Evolution for Agents in Policy-Governed Enterprise Workflows
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
Record details
Published: 3 August 2026
Source: arXiv
Category: Research
Topics: Regulation · Agents & autonomy · Transparency
Retrieved: 4 August 2026
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How to cite this record
ethics.ai (3 August 2026), “FRAMES: Guarded and Dual-Objective Skill Evolution for Agents in Policy-Governed Enterprise Workflows,” evidence record 15900, https://ethics.ai/record/15900 (originally published by arXiv).
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