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This paper introduces a framework for quantifying human-agent collaboration, defining a "leverage ratio" as the amount of human work displaced by an agent, divided by the human time required for task specification, interruption resolution, and result review. It decomposes this ratio into information flow channels and identifies bounds on information density between humans and agents. The framework extends the per-task analysis to a windowed measure accommodating recurring tasks and amortized system design investment, providing a normative ratio for supervisory control and mixed-initiative interaction.
Quantifying the efficiency of human-AI collaboration boils down to balancing the agent's work output against the human's time investment in task specification, interruptions, and review.
We propose a per-task leverage ratio for human-agent collaboration: human work displaced by an agent, divided by the human time required to specify the task, resolve mid-run interrupts, and review the result. The denominator decomposes into three channels through which a conserved per-task information requirement must flow, each with its own time-cost scalar. We show that information density itself is directional and bounded by separate ceilings on human-to-agent and agent-to-human flow, and that the asymptotic behavior of leverage decomposes into two scaling axes (capability and memory) with a non-zero floor on the planning term set by irreducible task novelty bounded by human throughput. We extend this per-task analysis to a windowed leverage measure that accommodates recurring tasks, spawned subtasks, and amortized system-design investment. The per-task ceiling does not bind the windowed measure, though both remain bounded: $L_{\text{task}}$ by per-task novelty, $L_{\text{window}}$ by the stock of accumulated planning investment that pays out within the window. The framework operationalizes aspects of earlier qualitative work on supervisory control (Sheridan, 1992), common ground (Clark&Brennan, 1991), and mixed-initiative interaction (Horvitz, 1999) within a single normative ratio, and produces a list of testable empirical questions that we leave as open problems.
