GoKawiilAI agents have become much better at building and editing Excel workbooks over the past six months but still struggle to evaluate them. Agents especially struggle in environments where the Excel application isn't installed, an increasingly common bottleneck as AI moves to always-on, scaled cloud setups. In those environments, agents may silently fall back to shadow analysis in hidden Python scripts.
Analysis with Excel
Working with Excel involves two main components:
the workbook file, and
the Excel application, which interprets the file and performs calculations.
Excel Files
Accessing a file's content is straightforward in most cases. The xlsx format is a zipped archive containing a collection of files, most of them written in highly structured XML. On Windows or macOS, you can inspect xlsx files by changing the file extension to .zip or opening the workbook with an archive utility.
Cell contents are defined in worksheet XML files. For example, a cell might be represented as follows:
< c r = "C20" s = "7" t = "n" > < f aca = "false" > C19*$B$6 </ f > < v > 40 </ v > </ c > < c r = "C20" s = "7" t = "n" > < f aca = "false" > C19*$B$6 </ f > < v > 40 </ v > </ c > < c r = "C20" s = "7" t = "n" > < f aca = "false" > C19*$B$6 </ f > < v > 40 </ v > </ c >
The <c ...> ... </c> element contains the complete definition of cell C20 . The inner <f> element stores the cell's formula, =C19*$B$6 , while the <v> element holds the most recently cached result, if available. Excel updates this value when it recalculates and saves the workbook.
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