Research paper 31

Agentic Temporal Compression: Iteration Density and Retrospective Distance in AI-Assisted Software Development

AI-assisted software development can change the density of work without changing the clock. A developer can complete prompt-response-action loops, tool inspections, code patches, tests, reversals, and design decisions in a single day. In calendar time, little time has passed. In project time, the system may have crossed several meaningful states. This paper proposes a construct for that discrepancy: agentic temporal compression, the subjective experience that recent project history feels unusually distant because a fixed interval contains unusually dense meaningful change. The paper separates the proposed workflow variable from the proposed subjective outcome. Iteration density is the number of meaningful development cycles, model-mediated updates, decisions, experiments, or architecture revisions completed per unit of clock time. Agentic temporal compression is the retrospective experience that recent work feels older, farther away, or more historically remote than its calendar age suggests. The contribution is not evidence that the phenomenon is widespread or caused by large language models. The contribution is a testable framing that connects agent-assisted development to established work on event segmentation, prediction error, temporal context, episodic memory, and retrospective duration judgment. The central hypothesis is that dense agentic workflows increase prediction-error events and mental-model revisions. Those revisions create salient event boundaries. Event boundaries and retrievable changes then increase the remembered density of an interval, making recent project states feel temporally distant. The paper outlines a staged research program: qualitative interviews, diary and telemetry studies, within-subject controlled tasks, and longitudinal field observation during transitions into agentic workflows.