Epistemic Architecture of Accountable Artificial Agents: Justified Acceptance Paths and the Horizon-Warrant-Commitment Framework

Contemporary AI agents exhibit high productivity but lack epistemic accountability: they can produce correct outputs without any principled answer to why a given judgment was permitted. This gap is not merely philosophical -- it is social. When agents act on behalf of institutions, societies require not just correct outputs but legitimate decisions: evidence of what was considered, under what conditions it was admitted, and where responsibility lies when things go wrong. This paper argues that closing this gap requires reconceiving agent architecture from the ground up. We analyze the Structured Cognitive Loop (SCL) framework as an implementation of three structurally distinct justified acceptance paths: Memory Fact, grounded in causal tool observation; Contextual Human-in-the-Loop (HITL), grounded in authoritative human judgment; and Pool-Gated Retrieval-Augmented Generation (RAG), grounded in threshold-governed evidential selection. All three paths are unified by the Grounding Principle, which fixes the epistemic basis of any reasoning episode at turn onset and prevents circular epistemic drift. The paper's central contribution is the tripartite framework of Epistemic Horizon, Warrant, and Commitment (H-W-C). This framework was not imported from epistemological theory; it emerged from philosophical analysis of SCL's actual operation. The paper further argues that H-W-C constitutes a general formal condition: any epistemic input can be incorporated into an accountable artificial agent if and only if its H-W-C conditions can be structurally specified. The central thesis is that intelligence appropriate for accountable agents is normative rather than descriptive -- not a matter of what the agent can produce, but of what it is entitled to produce, and under what structural conditions that entitlement is conferred.