PlanMagnet’s AI ambition is deliberately narrow: four decisions, each grounded in data the workspace already owns. Rather than ship a chat box and call it intelligence, the product is being built one decision at a time, and this story is the running scorecard.
Draft work from an idea is the one that exists. A description goes to the model, and a validated list of work-item suggestions comes back — typed, prioritised, capped, and returned as suggestions only. The mutation is live in the PlanMagnet service behind a permission check, and it deliberately cannot write to your backlog; a person picks what to keep and the ordinary create path persists it. Nothing lands un-reviewed.
Summarise a thread into the decision and the next action is designed but not built. The design constraint that matters is that summarisation happens server-side: raw message bodies never leave the workspace governance boundary just to produce a summary.
Forecast a delivery date is partly there in a deterministic form. PlanMagnet already computes velocity across trailing completed sprints and offers a next-sprint number from that trailing average — honest arithmetic, not a model, and labelled as such. The designed upgrade is a forecast that returns a date range with a confidence signal and cites the specific sprint IDs and velocity values it was grounded in, so an engineering lead can check the maths before repeating the date to leadership.
Flag at-risk work is designed to name a reason per item — stale for N days, blocked by a specific dependency, assignee over-committed, sprint burn-rate implies a miss — rather than emit an opaque score.
One of these four decisions is live in the service today and the other three are committed roadmap; the individual AI Native stories alongside this one say which is which, per capability. This story is narrative-only today; the interactive app walkthrough is not yet available.
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