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

Problem
This paper addresses a gap in the understanding and measurement of Agentic Technical Debt and Stochastic Tax within the context of Agentic AI systems. The authors highlight that while existing literature discusses the implications of technical debt in software engineering, there is a lack of formal frameworks that differentiate between accumulated design liabilities (Agentic Technical Debt) and the operational burdens (Stochastic Tax) that arise from the use of stochastic agents in business workflows. This work is presented as a preprint and has not yet undergone peer review.

Method
The authors propose a standalone framework that consists of a compact dashboard expression, which is expanded into a comprehensive structural model. The framework defines all relevant variables and parameters associated with Agentic Technical Debt and Stochastic Tax. The model allows for the estimation of various cost categories using operational data. The authors illustrate the framework through a simulation focused on accounts payable, providing a companion spreadsheet for practical application. The technical contribution lies in the formalization of these constructs and their interrelationship, enabling managers to quantify and visualize the impact of technical debt and stochastic tax on operational efficiency.

Results
The paper does not provide quantitative results in the form of headline numbers or comparisons against established baselines, as it primarily focuses on the theoretical development of the framework. However, the authors demonstrate the utility of their model through a simulation example, which showcases how the framework can be applied to real-world scenarios. The effectiveness of the model in estimating costs and visualizing the relationship between Agentic Technical Debt and Stochastic Tax is implied but not quantitatively validated against existing benchmarks.

Limitations
The authors acknowledge that their framework is a conceptual model and may require further empirical validation to establish its effectiveness in diverse operational contexts. They do not address potential challenges in data collection for estimating the defined variables, nor do they discuss the scalability of the model across different industries or the potential for overfitting in simulations. Additionally, the lack of quantitative results limits the ability to assess the practical impact of the framework on decision-making processes.

Why it matters
This work has significant implications for the management of Agentic AI systems, particularly in understanding the financial and operational impacts of technical debt and stochastic agents. By providing a structured approach to measure and simulate these constructs, the framework can aid organizations in making informed decisions regarding the deployment and governance of AI technologies. The differentiation between Agentic Technical Debt and Stochastic Tax allows for more nuanced risk assessments and resource allocation strategies, ultimately contributing to more efficient and effective AI-driven workflows. This framework could serve as a foundation for future research aimed at refining measurement techniques and exploring the broader implications of technical debt in AI systems.

Authors: Muhammad Zia Hydari, Raja Iqbal, Narayan Ramasubbu
Source: arXiv:2605.27320
URL: https://arxiv.org/abs/2605.27320v1