Beyond Static Interrupts: Context-Aware Human-in-the-Loop as a Cognitive Process for Trustworthy LLM Agents

Large Language Models (LLMs) have demonstrated remarkable generative capabilities, yet their deployment as autonomous agents continues to suffer from fundamental limitations in accuracy, reproducibility, transparency, and accountability. While recent work has focused on scaling model size or refining training objectives, this paper argues that these limitations are not primarily learning problems, but architectural ones. We adopt an epistemic view of intelligence, in which reasoning quality is determined not by internal model capacity alone, but by how knowledge is acquired, validated, constrained, and acted upon within a structured system. From this perspective, LLMs are best understood as high-quality proposers rather than ultimate decisionmakers, with responsibility for judgment, memory consistency, and control residing at the system level. Building on the Structured Cognitive Loop (SCL) architecture, we present a context-aware Human-in-the-Loop (HITL) mechanism that integrates human intervention as a first-class cognitive event rather than a static safety interrupt. We introduce the concepts of cognitive state freezing and thawing, virtual rejection cycles, and tracegrounded resumption, enabling coherent pause-resume behavior without loss of epistemic continuity. All decisions, including human approvals and rejections, are recorded as verifiable cognitive transitions within a Glassbox Trace. We illustrate through a reference implementation how this architecture achieves high accuracy, reproducibility, and auditability even with moderately sized language models, and argue that certain properties of trustworthy agency-particularly responsibility delegation and normative intervention-cannot be achieved through model scaling alone. This work positions HITL not as an operational safeguard, but as an essential component of intentional, accountable AI systems.