We describe Abjad’s submission to the AISA-ArabicFC shared task (Track B), which targets Arabic function calling with explicit natural-language reasoning prior to the structured call. Rather than fine-tuning, our system builds a robust inference and evaluation pipeline around the organizers’ baseline model, AISA-AR-FunctionCall-Think, diagnosing and fixing two pipeline-level bugs that were silently depressing scores. On the 545-example development set, our system reaches a function-name accuracy of 96.15% but an argument exact-match of only 40.6%, yielding an official Track B score (overall_b) of 0.6665, below the organizer-reported baseline of 0.739. We present a fine-grained error analysis showing that failures concentrate almost entirely in argument value formatting, including currency codes, ISO language codes, and transliteration, rather than in tool selection or task comprehension. These findings suggest that targeted fine-tuning or post-processing for structured-value normalization is a promising direction for improving Arabic function-calling performance. abjad aisa arabicfc.pdf