Set up data, reconciliation, variance, forecast and Reporting Watch agents that work from accepted figures, route exceptions to a finance reviewer and can be paused or retired.
What you will be able to do
- Turn arriving files into one traceable dataset: Define an intake agent that sorts each batch into new, restated, duplicate or rejected files. It builds a versioned dataset where reporting period, currency, units and source stay visible on every row, and rejected or duplicate files stay out of totals.
- Reconcile every accepted version without silent fixes: Build a reconciliation agent that runs agreed matching rules and tolerances. It records each check and its evidence in a ledger matching workbook and tie-out log, and sends exceptions to a reviewer instead of making adjustments.
- Keep variance commentary tied to evidence: Create a variance review agent that refreshes the review when reconciled results change. It produces a driver bridge that adds up to the total, and it keeps a possible cause out of management commentary until evidence supports it.
- Refresh forecasts without blurring the model: Set up a driver-update agent that recalculates an approved sample cash model only when an approved assumption changes. The model, the assumption register and the resulting scenarios stay separate, so you can see which assumption set each figure came from.
- Run a Reporting Watch agent on a schedule you agree: Configure a named Reporting Watch role on OpenAI Dots or Claude Duet. You set agreed intervals, change triggers, approved connections, agreed reports and a named reviewer, and choose between a single run and a continuing brief.
- Test, then pause or retire with a clear record: Test your agents against missing, invalid, extreme, wrong-unit, stale and late inputs across several simulated periods. Then decide whether to continue, adjust, pause or retire the work, and record why.
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