Responsibility Completion for Agentic AI

Responsibility Completion for Agentic AI establishes a foundational reference for determining when accountability obligations in autonomous, agent-to-agent (A2A) AI systems can be considered conclusively closed. As AI systems evolve from human-assisted tools into autonomous agents capable of negotiation, delegation, and arbitration, the limiting factor for enterprise adoption is no longer compute, model performance, or security hardening. The critical unresolved constraint is the absence of a verifiable termination condition (closure condition) for responsibility. This work introduces Conceptual Sovereignty as a first-class requirement for agentic AI systems, asserting that AI may process human inputs without internalizing the underlying conceptual structures that generated them (Readable ≠ Learnable). Building on prior research that defined Cognitive Leakage and the Delta-1 (Δ1) Validity Condition, this document specifies the architectural conditions under which an AI interaction can be treated as responsibility-complete. Scope and Non-Implementational Stance Importantly, this publication does not describe implementation mechanisms, algorithms, training techniques, memory architectures, or enforcement logic. Instead, it defines: A definitive shift in threat modeling from Data Exposure to Concept Capture The conceptual closure conditions required to terminate responsibility in A2A interactions Minimum Adoption Standards suitable for procurement, audit, and system acceptance Responsibility Artifact classes—including Disclosure Packs, Dispute Replays, and Proof Packs—intended for verification and dispute resolution, without prescribing schemas or technical formats This document is intentionally vendor-neutral, platform-agnostic, and non-implementational. It does not claim that any existing system satisfies the defined conditions, nor does it function as a compliance checklist or certification. Its role is to provide a shared conceptual reference against which systems, controls, and claims can be evaluated. Intended Audience The intended audience includes enterprise decision-makers, system integrators, platform providers, auditors, insurers, and policymakers seeking to enable large-scale deployment of autonomous AI systems without incurring unbounded or retroactive responsibility exposure. Related Work This standard builds upon the theoretical framework established in: Chang, Y. (2026). Cognitive Leakage: A Unified Framework for AI Accountability in the Age of Autonomous Agents. Zenodo. https://doi.org/10.5281/zenodo.18366186