MobileGuard: A Stack-Agnostic Governance Framework for Agentic AI Across Consumer Mobile Delivery Platforms

Consumer mobile platforms have emerged as the primary deployment surface for agentic AI features — from on-device small language models executing multi-step GUI tasks to ambient platform AI (Apple Intelligence, Siri, Google Gemini Nano) acting on users' behalf across apps. Yet every published governance framework for agentic AI assumes a cloud or enterprise backend where rapid patching, server-side rollback, and continuous monitoring are available. Mobile introduces four hard constraints invisible to existing frameworks: (1) binary immutability post-deployment, where no hotfix reaches users without App Store or Play Store review; (2) simultaneous consumer-scale impact, where a quality failure affects millions before any response is possible; (3) platform gatekeeper review as an independent quality layer with its own non-deterministic acceptance criteria; and (4) ambient agentic surfaces that operate partially outside the app developer's control. We present MobileGuard, the first governance framework designed specifically for these constraints. MobileGuard comprises four pillars: Pre-Deployment Quality Contracting (PDQC), Tiered Autonomy Calibration for Mobile (TAC-M), Platform Gatekeeper Simulation Gates (PGSG), and Ambient Agent Boundary Enforcement (AABE). We validate MobileGuard through two studies: a systematic analysis of 2,847 App Store rejection records showing that 61% of AI-attributable rejections are undetectable by existing governance frameworks, and a simulation study on an instrumented agentic iOS delivery pipeline showing that MobileGuard reduces defect escape rate by 74% and autonomy miscalibration events by 83% compared to an ungoverned baseline. Our results demonstrate that mobile-specific governance constraints are not a subset of enterprise AI governance — they constitute a qualitatively different problem requiring a purpose-built framework.

@misc{singh2026mobileguard,
  title={MobileGuard: A Stack-Agnostic Governance Framework for
         Agentic AI Across Consumer Mobile Delivery Platforms},
  author={Singh, Jaspreet},
  year={2026},
  eprint={XXXX.XXXXX},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  url={https://arxiv.org/abs/XXXX.XXXXX}
}