AgencyBudget: Controlling Unsolicited Change in Iterative Human–AI Co-Creation

Creative AI systems increasingly act as collaborators rather than one-shot generators, but “taking initiative” is usually treated as a qualitative interaction property rather than a quantity a user can control. We introduce unsolicited change: the normalized amount by which an AI edits attributes outside the user’s current request, and use it to define an explicit agency budget. We then propose AgencyBudget, a backbone-agnostic controller that projects candidate creative edits into the feasible budget set before selecting the highest-utility edit. To isolate the control problem from generator quality, we build a reproducible multi-turn vector-poster testbed with 20 normalized layout and appearance variables, evolving user constraints, heterogeneous aesthetic preferences, and controllable proposal volatility. Across 2,520 held-out edit steps at budget B = 0.01, AgencyBudget attains zero budget violations and a proxy aesthetic score of 0.806 ± 0.004, compared with 0.785 ± 0.004 for post-hoc clipping. A globally tuned penalty baseline reaches 0.814 ± 0.004 but exceeds the per-edit budget on 30.3% of edits; an unconstrained agent violates it on 78.3%. Under a sixfold change in proposal scale, AgencyBudget remains exactly calibrated while penalty-based control drifts. These results do not establish improvements in human creativity; instead, they show that creative initiative can be operationalized as an enforceable resource, making the boundary between following and steering explicit.