Having seen some spreadsheets in my time, I can imagine what the training data is like.

There are way worse choices to fall back on than LibreOffice. Heck, it's the primary tool I actually use in my business. I can tell you first hand that it does not handle all the random stuff that corporate Excel users come up with, but if you start with basic principles the files usually work great in the other direction.

My agents can deal with Excel all right. What’s the story?

I once tried using Gemini directly in Google Sheets to make a graph. I never managed to get it to do exactly what I wanted; after wasting way too much time on it I was able to fix the problem in a matter of seconds. It's fascinating because it is quite different from the situation in coding agents where they are in general really quick to read code and find issues/fixes.

I didn't know that. Agents can't do today what I used VB for 20 years ago?

Have your agent call DuckDB to load it first.

The key is to have agents generate code which exports Excel with live formulae (there's a good Python library).

Building financial models in python will never work

The end user is not a software developer

I've tried to 'reverse engineer' some of our excel sheets, and it's difficult to get the model to stop scolding me for using so many =INDIRECT statements.

False.

> Orcaset takes an entirely different approach to financial modeling. It builds financial models in pure Python code rather than attempting to interpret xlsx files. Model calculations are fully traceable, deterministic, and programmatically accessible by agents. Additionally, since Orcaset isn't limited to a spreadsheet's two-dimensional grid of A1:B2 cells, it can leverage software best practices like type checks to guard against invalid models.

Note that this is just an ad for Orcaset. But more interesting, if you look at Orcaset, they say its product is "Financial Models as Code". So instead of using Excel, you use Python. Reasonable and most serious models are in code. But I still can't figure out what Orcaset adds. It looks like the worst of both worlds. You have this cell concept, but it doesn't relate to an actual workbook so it's not transparent. And then you have the magic formulas and decorators that you have to learn.

@PeriodSeries.define("Revenue", accrual(YF.cmonthly))

def revenue():

    for k in Period.seq(date(2026, 1, 1), relativedelta(months=1)):

        def cell(p: Period = k):

            prior = yield from get_at(revenue, p.shift(-relativedelta(months=1)))

            return prior * 1.10 if not isna(prior) else 100.0

        yield k, factory

costs = (revenue * -0.50).named("Costs")

profit = (revenue + costs).named("Profit")

flag because submarine article.