Human Oversight in LLM-Powered Workflow Automation: A Focused Narrative Review of Hallucination Mitigation, Confidence-Based Routing, and Operational Trade-offs

This paper presents a focused narrative review of human oversight in large language model (LLM)-powered workflow automation. It examines the challenges posed by hallucinated or unsupported model outputs and discusses approaches including retrieval-augmented generation, uncertainty estimation, confidence-based routing, selective prediction, and human-in-the-loop review. The paper develops a conceptual framework for considering when uncertain outputs should be directed to human reviewers in automated workflows. It also outlines evaluation measures for assessing correctness, end-to-end latency, human intervention rates, reviewer corrections, and error severity. The discussion highlights operational trade-offs between reliability, review workload, and automation efficiency, along with considerations for auditability and risk-sensitive oversight. Scope and limitations: This is a focused narrative review and conceptual framework. It does not report a newly conducted experiment or empirically validate a deployed workflow. Implementation and experimental evaluation are identified as directions for future research.