This exploratory longitudinal N-of-1 study examines why one participant found a long-running interaction with a frontier model easier to coordinate than fresh instances. It tests several explanations rather than assuming a new mechanism. Different state-compression strategies did not establish a utility advantage, and compact state performed at a high quality level without a useful directional advantage over richer or full-trajectory continuation. A Claim Lifecycle governance prototype also showed no measurable advantage over a lightweight baseline: both succeeded in 7 of 7 cases. A 496-word static coordination packet likewise failed to establish a practical benefit, improving only one of five burden dimensions; although the participant experienced the packet session as substantially smoother, blind coding recorded more wrong-branch episodes with the packet, five versus four. The results narrow the open question to whether ongoing human-AI calibration preserves information that static representations cannot. The project also documents a workflow in which human judgment, conversational AI formalization and review, coding-agent implementation, executable prototypes, adversarial and regression tests, frozen evaluation contracts, and machine evidence supported the participant’s research despite limited programming experience. The authors stress that this is not proof that programming expertise is unnecessary, but an N-of-1 capability-access case released for criticism, literature connections, and methodological feedback rather than endorsement or proof of novelty.
