Multi-agent LLM results can conflate decomposition with roles, communication, parallelism, and extra compute. We study strictly ordered tool-use workflows of dependent state-changing steps, where segmentation may also support auditing and handoff constraints. We introduce Trace-GRAF, a granularity selector that aligns one single-agent trace to target workflow steps, converts order violations and unrelated drift into split pressure, and selects the smallest granularity on the minimum-pressure plateau. It executes balanced contiguous segments because boundary ablation finds no significant success difference from semantic segmentation; pressure-minimizing partitions characterize cuts. Across held-out cohorts spanning two benchmarks and three executors, fixed decomposition sometimes improves and sometimes reduces success, while greater granularity both rescues failures and regresses solved runs. When SA success is observable, the pressure route preserves successful runs by construction. Across validation resplits on TerminalWorld, the calibrated extension’s mean recovery is 2.3–3.0% for GPT-OSS, 0.0–0.1% for Gemini, and 2.3–4.9% for DeepSeek. Simple fixed fallbacks match much of the observed gain. Trace-GRAF therefore provides a risk-aware routing diagnostic with benchmark- and split-dependent gains.

Large-scale scientific research on historical documents — particularly medieval Arabic manuscripts — remains challenging due to the need for advanced paleographic and linguistic training, the large volume of hand-written materials, and the absence of assisting software. In this paper, we propose InkSight, the first end-to-end Arabic manuscript analysis tool for manuscript-based analytics and research hypothesis testing. InkSight integrates three key components: (i) an Optical Character Recognition (OCR) module utilizing a Large Visual Language Model (LVLM); (ii) a lightweight document indexing and information retrieval module that enables query-based evidence retrieval from book-length manuscripts; and (iii) a flexible Large Language Model (LLM) prompting interface factually grounded to the given manuscript via Retrieval-Augmented Generation (RAG). Empirical evaluation on the existing KITAB OCR benchmark and our in-house dataset of ancient Arabic manuscripts has revealed that historical research can be effectively supported using smaller fine-tuned LVLMs without relying on larger proprietary models. The live web demo for InkSight is available freely at: https://inksight.ru and the source code for InkSight is publicly available at Github: https://github.com/ds-hub-sochi/InkSight-tool.