Decision Graph

A decision graph is the structured memory of why a product is built the way it is. Each decision links to the hypotheses it validates, the evidence that shaped it, the alternatives rejected, and the later decisions that supersede it.

AI agents can execute faster than teams can explain context. A decision graph gives agents and teammates a stable source of product reasoning, so execution stays aligned with intent.

A useful decision record names the problem, the chosen approach, the alternatives, and the evidence behind the choice. It also records who owns the decision and which plans or tasks depend on it. Links let readers follow the reasoning without searching through a folder of meeting notes.

Treat an unreviewed proposal differently from an accepted decision. A customer quote may support a choice without proving it. Keeping those distinctions visible helps a team understand which constraints are settled and which assumptions still need testing.

Suppose a team chooses a guided setup instead of an empty dashboard. The decision links to interviews where new users could not find their first action, a hypothesis about activation, and the implementation plan. An agent working on onboarding can then preserve the intended first action while changing the interface.

If later evidence favors a simpler setup, record the replacement decision and connect it to the old one. Preserve the old rationale for explanation, but make the current choice clear. A graph should help people distinguish history from the instructions that apply today.

A chronological log answers when a decision happened. A graph also answers what the decision affects and which evidence could overturn it. Start with a small set of consequential choices and meaningful links; recording every minor edit makes the important reasoning harder to find.