An AI agent rarely follows a long plan exactly as first proposed. Tool results expose missing facts, external systems change, policies arrive through human input, and a model may discover that an earlier assumption was wrong. ReAct-style agents were explicitly designed around this interleaving of reasoning, action, observation, and plan updates rather than one immutable plan. The engineering difficulty begins when those actions are durable side effects. In Temporal, a completed Activity is not an intention that can be edited out of a revised plan; its completion and result are part of the Workflow Event History. Replanning therefore has to reconcile a new decision with an already-real past.