Cost per Completed Task: A Measurement Framework and Cross-Domain Case Study for Evaluating AI Model Configurations in Agentic Workflows

This paper proposes Cost per Completed Task (CPCT) as a system-level measurement framework for comparing AI model configurations in agentic workflows. CPCT aggregates input/output token cost, cache usage, tool and sandbox cost, wall-clock runtime, failed-attempt cost, and human-review effort into a single task-centred denominator: accepted work. A real cross-domain case study (forensic report generation) shows an 8.6x cost swing on identical model and task driven purely by operational overhead.