Agent Epistemic Accountability Layer: A Framework for Runtime Verification and Trust Calibration of Autonomous AI Agent Outputs

Agent Epistemic Accountability Layer (AEAL) is a framework designed to provide runtime verification and trust calibration for autonomous AI agent outputs. As AI systems increasingly operate with partial autonomy across scientific, industrial, and operational domains, the need for transparent, inspectable, and behaviorally accountable agent reasoning becomes critical. AEAL introduces a structured mechanism for evaluating the epistemic soundness of agent‑generated claims, decisions, and actions. This report defines the conceptual architecture of AEAL, outlines its verification pathways, and presents a methodology for assessing agent behavior under uncertainty. The framework is intended as a foundational component within the emerging field of AI Behavior Science, complementing the broader territory paper (AI Behavior Science — Founding Territory Paper, DOI: 10.5281/zenodo.19562751). AEAL v1.0 establishes: A principled model for epistemic state tracking in autonomous agents Runtime verification procedures for agent outputs Trust calibration mechanisms based on evidence, uncertainty, and behavioral patterns A blueprint for future instrumentation, evaluation protocols, and cross‑model comparisons This work is part of an ongoing effort to build a rigorous scientific foundation for understanding, evaluating, and governing autonomous AI agent behavior.