Physics-aware multimodal large language models (MLLMs) can localize and characterize two-dimensional (2D) material flakes from optical microscopy images, producing rich candidate observations and scientific reasoning. However, downstream workflows also require reliable operations over these outputs, including counting, selection, measurement, and rendering. We present OpenQlaw, a tool-using agentic artificial intelligence (AI) system that connects a general vision-language orchestrator with Physics-Aware Instruction Tuning (QuPAINT), a physics-aware domain expert. Through evaluation on fully reviewed optical microscopy images, we show that summary counts derived from longer free-form candidate enumerations can become inconsistent with the underlying returned observations. Controlled replay experiments further show that structuring the same observations alone does not resolve this behavior, while deterministic delegation returns the exact requested record count in all cohort trials. To address this, we introduce \textbf{State-Grounded OpenQlaw}, which converts expert outputs into image-scoped observation records and performs deterministic scientific operations over explicitly identified records with refusal conditions when required information is unavailable. This preserves the expert's original analysis while making downstream operations exact over the selected record and independently evaluable from candidate-list discrepancies.

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