A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP
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). The proposed Context Engineering via DT-MDP (DT-MDP-CE) framework comprises three key components: (1) A Digital-Twin Markov Decision Process (DT-MDP), which abstracts the agent's reasoning behavior as a finite MDP; (2) A robus...
| Authors | Xi Yang, Aurelie Lozano, Naoki Abe et al. |
|---|---|
| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 23 March 2026 |
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