Governing Agentic AI: The Human Values Alignment Framework (HVAF-A) as a Policy Tool

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).

Agentic artificial intelligence (AI) systems—software that plans, acts, and decides without step-by-step human approval—are already deployed in finance, healthcare, recruitment, and public services. They differ from earlier AI in one critical way. They do not produce outputs for a human to accept or reject. They take actions. Those actions may be irreversible. They may affect people who never used the system. Existing governance frameworks were not designed for this. Current alignment approaches—including reinforcement learning from human feedback, constitutional AI, and preference aggregation—assume that a well-aligned system satisfies what users ask for. This paper argues that the assumption fails in agentic contexts. What people ask for is not what they value. Preferences are volatile and user-centric. Values are stable, culturally grounded, and other-regarding. This paper proposes the Human Values Alignment Framework for Agentic AI (HVAF-A), grounded in Schwartz’s cross-culturally validated Basic Human Values theory. The framework connects value inputs, three alignment mechanisms (elicitation, arbitration, and propagation), and governance outcomes at individual, organizational, and societal levels. The paper positions the HVAF-A against the main international policy instruments—the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001, and the OECD and UNESCO principles—and specifies an empirical program of constructs, measures, validity tests, and study designs. An illustrative case study of an agentic hiring system shows how the framework would operate. This is a purely conceptual study. No study was conducted and no instrument was administered to validate the model. A dedicated section states the framework’s boundary conditions. Empirical validation is set out as future research.