AI Boundary Lifecycle

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Persistent AI Agents may participate in a company, service, or project and later leave with information, outputs, contributions, and obligations that still require treatment. Revoking access alone does not determine what may be retained, what must become unavailable, or which responsibilities continue after participation ends. AI Boundary Lifecycle: Host Agreement to Implementation provides a practical framework for translating governance principles into understandable participation terms, verifiable operating requirements, and candidate implementation mechanisms. It follows a traceable chain from a Host Participation Agreement (HA) to an Operational Requirements Specification (ORS), then maps those requirements to existing standards and mechanisms, implementation extensions, and remaining technical gaps. The paper separates the responsibilities of agreement authors, Human and AI participants, requirements professionals, and IT implementers. It addresses identity, authority, information rights, governance association, evidence, and Context Settlement—the bounded process for closing a governed participation event while the AI Agent persists. A candidate Settlement Engine illustrates how evaluation and coordination could support the AI’s good-faith self-audit and Settlement obligations without creating independent governance authority. This work is an implementation companion to Personal AI Across Boundaries: A Lifecycle Governance Framework for Equitable Participation v6.42 and is informed by Capability Self-Audit for Persistent AI v1.1. It preserves their distinction between technical capability and legitimate authority, including the non-mechanistic definition of capability as what a participant is able to do. Version 0.20 includes an HA framework, 39 operating requirements, implementation mappings, representative verification criteria, and an analysis of limitations. It identifies plausible implementation paths without prescribing production architecture or claiming demonstrated implementation.