Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding
Muhammad Zia Hydari, Raja Iqbal, Narayan Ramasubbu
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Key claim
Agentic Technical Debt amplifies Stochastic Tax in AI systems.
This paper presents a model that differentiates between two important concepts in AI systems: Agentic Technical Debt and Stochastic Tax. The key result is that while debt can amplify operational burdens, the tax can persist even with minimized debt, offering insights for better governance in AI workflows.
In plain English
This paper presents a model that differentiates between two important concepts in AI systems: Agentic Technical Debt and Stochastic Tax. The key result is that while debt can amplify operational burdens, the tax can persist even with minimized debt, offering insights for better governance in AI workflows.
The paper introduces a new model distinguishing between Agentic Technical Debt and Stochastic Tax, which is a meaningful extension of existing concepts in AI systems.
The framework is illustrated with a simulation and operational data, providing a solid basis for the claims made.
Deep reliability assessment
The methodology provides a structured framework for measuring and simulating agentic technical debt and stochastic tax, but its effectiveness relies heavily on accurate calibration and expert judgment, which may not be universally applicable.
Reproducibility
No open source code or dataset is provided in the paper.
Discussion questions
- How does the model account for the variability in different organizational contexts and workflows?
- What are the practical challenges in implementing this framework in a real-world setting?
- What evidence would demonstrate that the distinction between agentic technical debt and stochastic tax is not useful in practice?
Key figure
Figure 1 illustrates the per-transaction stochastic tax across four scenarios, showing how adoption and debt levels affect costs.