Middle school mathematics teachers using a multi-agent LLM system routinely overrode agent assessments to refine real-world problem contexts, exposing limits in automated authenticity detection that mainstream AI education coverage consistently overlooks. The arXiv:2604.12066 paper details how eight teachers generated 212 problems in ASSISTments, feeding base problems and topics into an LLM that four specialized agents then evaluated for mathematical accuracy, authenticity, readability, and realism. Teachers and students primarily requested modifications to fine-grained personalized elements rather than broad categories agents flagged, with realism issues rarely noted in final assigned versions despite agents surfacing many during creation; readability and hallucination problems remained infrequent. This human-in-the-loop pattern aligns with Walkington & Bernacki (2020, Journal of Educational Psychology) on interest-driven personalization in ASSISTments, where teacher curation improved engagement metrics, and Wu et al. (2023, arXiv:2308.08155) on AutoGen multi-agent frameworks demonstrating that specialized agents boost consistency yet require domain-expert overrides for contextual fit in education. Original source coverage missed the iterative collaboration dynamics, underplaying how teachers' tacit knowledge of student backgrounds fills gaps in agent realism judgments, revealing hybrid workflows as the practical norm over full AI autonomy in real classrooms.