Fuse: Keeping an AI Team Within One Approved Task

An AI team can respect a spending limit and still exceed what its user approved. Two assistants may buy the same 25-credit report from different providers, spending only 50 of a 100-credit budget. Fuse keeps a shared record of the permission behind that purchase. Before a protected purchase executes, the system reserves both the money and the right to buy that report. An unanswered request keeps its reservation until its outcome can be established. We built a small Python reference model and compared two versions: a correct shared budget with safe retries, and the same controls with Fuse's shared purchase record. Across all 20 tested orderings of two purchase attempts, the first version bought the report twice in every ordering; Fuse bought it once. Both respected the budget and completed all four requested purchases in a separate normal-workflow check. Eighteen test methods passed. One cancellation ordering completed nothing, making the cost of conservative recovery visible. These are scripted engineering results under explicit assumptions, not observations of AI collusion. The contribution is an executable design, a conditional safety argument and a proposed behavioral evaluation. Fuse shows how a customer's purchase permission can remain intact across multiple attempts; its effectiveness with autonomous agents and real providers remains to be tested.