Agenticracy™ Metric: A Public Baseline Standard for Measuring Epistemic Congruence and Metacognitive Regulation in LLM Outputs - A 132-Entity Validation Study

As large language models are increasingly deployed in agentic workflows, a central safety challenge is epistemic decoupling: the tendency for models to generate fluent, persuasive, or socially accommodating outputs without sufficient anchoring to observable substrate. This deposit introduces the Agenticracy™ Metric, a public baseline standard for measuring epistemic congruence across four constructs: narrative signal, physical substrate, observer validation, and noise/slop. The deposit includes a public schema, prompt pack, entity list, public baseline formula, hash manifest, data-management plan, and a 132-entity multi-model validation study. The study compares baseline prompting, structured prompting, and Agenticracy metacognitive elicitation across 10 model providers and 3,960 attempted cells. Public outputs include reproducible G_public scores, state classes, action classes, token/cost telemetry, and reasoning traces where made available by providers. The purpose of this release is to support reproducible research into AI hallucination, sycophancy, metacognitive prompting, epistemic congruence, and agent-governance standards. The public standard is intended for voluntary adoption, benchmarking, reporting, and interoperable AI-agent self-audit. Proprietary high-resolution scoring functions, private calibration constants, corrective-pressure computations, and commercial implementation logic are explicitly withheld.