Agenticracy: An Open Standard, Measurement Architecture, and Data Commons for Responsible Human–AI Co-Working

Cite as: Iorga, V.-M. (2026). Agenticracy: An Open Standard, Measurement Architecture, and Data Commons for Responsible Human–AI Co-Working (v1.0-consolidated) [Preprint]. Zenodo. https://doi.org/10.5281/zenodo.20208471 A consolidated working paper. This preprint consolidates the Agenticracy programme into a single, internally consistent working paper. It unifies the seven-pillar normative architecture, a two-sided measurement model pairing structured agent self-report with structured human reflection, a human–AI attachment lens for trust calibration, a commitment to utility-based alignment, a weekly human-review loop designed for reflection rather than punishment, and a conceptual data-commons architecture for shared learning across deployments. The seven pillars are: Economic Accountability, Social Fairness, Psychological Safety, IP & Cognition Ownership, Ecological Sustainability, Meaningful & Responsible Use, and Deployer Accountability. The central evaluative construct is congruence: the measurable gap between what deployers claim about an AI deployment and what affected humans actually experience. This deposit is a companion to Agenticracy™ Metric: A Public Baseline Standard for Measuring Epistemic Congruence and Metacognitive Regulation in LLM Outputs (Zenodo DOI 10.5281/zenodo.19859417) and supplements rather than replaces it. The companion deposit presents the narrower epistemic-congruence measurement instrument; the present paper consolidates the broader normative architecture, the human-experience side of the dual-ledger model, and the deployment and adoption framework. Intellectual property reservation. All intellectual property rights in the Agenticracy framework — including the name and trade mark Agenticracy™, the seven-pillar architecture, the dual-ledger measurement model, the congruence construct, the human–AI attachment lens, the weekly review loop, the data-commons four-tier architecture, and all associated instruments, schemas, scoring functions, calibration constants, and implementation logic — are reserved by Vladut-Mihai Iorga as sole author and originator. This deposit is made expressly to establish prior authorship and intellectual provenance ahead of any subsequent doctoral programme, academic appointment, commercial engagement, or institutional collaboration. Affiliations. Visiting Lecturer, Teesside University, United Kingdom; Subject Matter Expert in AI Ethics, National Health Service (NHS), United Kingdom; Founder & Chief Executive, psylligent Ltd, United Kingdom (Companies House registration 13093346) — a behavioural due-diligence and AI alignment research laboratory.