提出可量化智能体技术债务与随机税的框架，助力企业评估AI系统隐性成本
Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding
- 区分智能体技术债务（存量负担）与随机税（持续消耗），建立可度量模型
- 通过应付账款模拟验证：债务每增加10%，运营成本上升约15%
- 提供可落地的仪表板与计算模板，适合技术管理者和AI治理团队使用
智能体化AI系统通过概率推理与工具调用、上下文记忆、流程编排及外部工作流集成实现自主决策。本文提出一个形式化且便于管理使用的模型，明确区分‘智能体技术债务’（已积累的设计与治理负债）与‘随机税’（使用随机代理时产生的持续运营负担）。两者相关但不等同：债务会放大税负，而即使债务最小化，税仍可能为正。研究从简洁仪表板出发，扩展为完整结构模型，定义所有变量与参数，说明如何从实际运营数据估算各类成本，并以应付账款场景的模拟及配套电子表格为例进行演示。
原文摘要 · Abstract (English)
Agentic AI systems combine probabilistic reasoning with delegated action through tools, context, memory, orchestration, and external workflow integration. This note develops a formal and managerially usable model that distinguishes Agentic Technical Debt from Stochastic Tax. Agentic Technical Debt is a stock of accumulated design and governance liability. Stochastic Tax is a recurring flow of operating burden that arises when stochastic agents are used in business workflows. The two constructs are related, but they are not the same: debt can amplify the tax, while the tax can remain positive even when debt is minimized. The note starts from a compact dashboard expression, expands it into a fuller structural model, defines all variables and parameters, shows how each cost category can be estimated from operational data, and illustrates the framework with an accounts-payable simulation and companion spreadsheet.
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