arXiv paper proposes 'substrate inversion' for sustained enterprise AI agent deployment

A new arXiv preprint argues that enterprise AI agents frequently work in demos but fail once they are asked to run continuously in production. The author attributes this to pilots that never ship and to deployed systems that discard feedback instead of learning from it, and proposes an approach called substrate inversion to address the gap. The work is categorized under cs.AI and is cross-listed on arXiv.