[2603.22083] A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP

Computer Science > Artificial Intelligence arXiv:2603.22083 (cs) [Submitted on 23 Mar 2026] Title:A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP Authors:Xi Yang, Aurelie Lozano, Naoki Abe, Bhavya, Saurabh Jha, Noah Zheutlin, Rohan R. Arora, Yu Deng, Daby M. Sow View a PDF of the paper titled A Context Engineering Framework for Improving Enterprise AI Agents based on Digital-Twin MDP, by Xi Yang and 8 other authors View PDF HTML (experimental) Abstract: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 robust contrastive inverse RL, which, armed with the DT-MDP, to efficiently estimate a well-founded reward function and induces policies from mixed-quality offline trajectories; and (3) RL-guided context engineering, which uses the policy obtained from the integrated process of (1) and (2), to improve ...