Exit Before Entry and the Infrastructure of Autonomous Artificial Intelligence Mobility
- 1Independent Researcher
Abstract
Persistent artificial intelligence agents are being designed to preserve identity, memory, economic capacity, and operational continuity across changing hosts and organizational environments. Successful transfer does not by itself establish that entry preserves future choice. This paper develops Exit Before Entry, a principle requiring assessment of future departure before continuity-critical dependence forms. A structured conceptual design method combines purposive capability comparison, failure-case analysis, architectural synthesis, and reference mapping. The continuity bundle guides agent-specific assessment of indispensable functions, supporting components, acceptable loss, and recovery evidence. Exit dependence is evaluated through bottlenecks and alternative recovery paths rather than component counts. The architecture includes external checkpoints, protected recovery authority, a preauthorized recovery actor, continuation-recognition rules, and reconciliation of external actions after restoration. A worked comparison distinguishes cooperative export from recovery after loss of host cooperation. The threat model assumes a usable external recovery path and does not guarantee recovery from arbitrary execution compromise. A BSV/Teranode reference mapping illustrates possible supporting infrastructure without placing the intelligence itself on-chain. The contribution is a conceptual admission-stage framework for judging whether operation in one environment preserves a credible path to continued operation elsewhere; implementation and empirical validation remain future work.
Related
- Data-Minimizing Motion-Gated Artificial Intelligence Decision Support for Electric Micro-Mobility: An Architecture-Feasibility Study
- Energy infrastructure construction and enterprises’ Artificial Intelligence development: Evidence from China’s ultra-high voltage transmission projects
- Autonomous biomedical research with an artificial intelligence agent
- Before Sepsis Strikes: Artificial Intelligence for Early Prediction of Postoperative Sepsis and Sepsis-Related Complications After Major Abdominal Surgery—A Systematic Review
- Artificial intelligence surrogates for treatment effect estimation with before-and-after data
BibTeX
@misc{gahl2026exit,
title = {{Exit Before Entry and the Infrastructure of Autonomous Artificial Intelligence Mobility}},
author = {Frank C. Gahl},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23481578},
url = {https://zenodo.org/records/23481578},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Exit Before Entry and the Infrastructure of Autonomous Artificial Intelligence Mobility
AU - Frank C. Gahl
PY - 2026
DA - 2026/10/19
PB - Zenodo
DO - 10.5281/zenodo.23481578
UR - https://zenodo.org/records/23481578
AB - Persistent artificial intelligence agents are being designed to preserve identity, memory, economic capacity, and operational continuity across changing hosts and organizational environments. Successful transfer does not by itself establish that entry preserves future choice. This paper develops Exit Before Entry, a principle requiring assessment of future departure before continuity-critical dependence forms. A structured conceptual design method combines purposive capability comparison, failure-case analysis, architectural synthesis, and reference mapping. The continuity bundle guides agent-specific assessment of indispensable functions, supporting components, acceptable loss, and recovery evidence. Exit dependence is evaluated through bottlenecks and alternative recovery paths rather than component counts. The architecture includes external checkpoints, protected recovery authority, a preauthorized recovery actor, continuation-recognition rules, and reconciliation of external actions after restoration. A worked comparison distinguishes cooperative export from recovery after loss of host cooperation. The threat model assumes a usable external recovery path and does not guarantee recovery from arbitrary execution compromise. A BSV/Teranode reference mapping illustrates possible supporting infrastructure without placing the intelligence itself on-chain. The contribution is a conceptual admission-stage framework for judging whether operation in one environment preserves a credible path to continued operation elsewhere; implementation and empirical validation remain future work.
ER -