AI RESEARCH
A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP
arXiv CS.AI
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ArXi:2603.22083v1 Announce Type: new Despite rapid progress in AI agents for enterprise automation and decision-making, their real-world deployment and further performance gains remain constrained by limited data quality and quantity, complex real-world reasoning demands, difficulties with self-play, and the lack of reliable feedback signals. To address these challenges, we propose a lightweight, model-agnostic framework for improving LLM-based enterprise agents via offline reinforcement learning (RL.