Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding

Researchers introduce a formal framework distinguishing Agentic Technical Debt from Stochastic Tax in AI systems that use tools and delegated actions. The model provides measurement, simulation, and dashboarding tools to help organizations quantify accumulated governance liabilities and recurring operational costs in agentic AI workflows.

This academic work addresses a critical gap in how organizations measure and manage costs associated with autonomous AI systems. As agentic AI—systems that combine reasoning, tool usage, memory, and external integrations—becomes more prevalent in business workflows, the hidden costs of maintaining and operating these systems remain largely unmeasured and poorly understood.

The framework distinguishes two cost categories that behave differently. Agentic Technical Debt represents accumulated design and governance problems that build up over time, similar to traditional technical debt in software engineering. Stochastic Tax, conversely, is an ongoing operational burden that emerges specifically because AI agents make probabilistic decisions rather than deterministic ones. This distinction matters because organizations might reduce debt without eliminating the recurring tax burden, suggesting that optimization strategies must address both separately.

For enterprises deploying agentic AI in mission-critical workflows—the accounts-payable simulation serves as a practical example—this framework enables quantification of previously invisible costs. Organizations can now estimate expenses from operational data and model scenarios before deployment. This standardization of measurement creates accountability around AI system efficiency and helps justify investment decisions.

The availability of a spreadsheet companion tool democratizes application of the framework beyond academic settings. As agentic AI systems proliferate in production environments, organizations lacking these measurement tools risk systematic underestimation of true operating costs and governance risks. The framework particularly benefits regulated industries and enterprises managing complex, multi-step workflows where agentic systems are increasingly replacing manual processes.

• →Agentic Technical Debt and Stochastic Tax are distinct cost categories requiring separate management strategies and measurement approaches.
• →The framework enables organizations to quantify previously invisible operational costs in AI agent workflows using existing operational data.
• →A practical spreadsheet tool accompanies the model, making implementation accessible for enterprises without specialized modeling expertise.
• →The distinction matters because minimizing debt does not automatically eliminate recurring stochastic tax from probabilistic decision-making.
• →Early adoption of this measurement framework provides competitive advantage in accurately budgeting AI system total cost of ownership.