Set up and supervise agents in OpenAI Dots or Claude Duet that watch approved signals, turn metric changes into questions, update resource scenarios and keep your decision record current.
What you will be able to do
- See relevant changes, not a news feed: Define a signal agent that checks a short list of approved sources on a schedule and reports only the changes that meet your materiality criteria. You test it with a known material change and a known non-material one.
- Get questions instead of guesses: Build a performance-review agent that turns each material metric change into a clear question for the responsible function. It also flags conflicting metrics and inconsistent period labels before they reach you.
- Keep resource tradeoffs visible: Configure a scenario agent that updates a small set of approved resource scenarios only when an approved assumption changes. Financial and operational constraints sit side by side, with a sensitivity table for the assumptions that matter most.
- Stop reconstructing past decisions: Maintain a leadership decision register with owners, review dates and changed assumptions. The agent proposes entries, a person confirms them, and the change history shows what moved and why.
- Follow up on what is overdue: Set escalation criteria that separate items for the owner from items for you. The agent drafts follow-ups for overdue dependencies and passed review dates, then assembles a recurring review pack from signals, performance, resources and commitments.
- Run agents you can pause and retire: Test your agents through delays, interrupted runs, missing inputs and several compressed cycles. Then pause, correct or retire them using defined triggers, and document it all in an operating sheet.
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