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Governing Technical Debt in Agentic AI Systems

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  • Muhammad Zia Hydari
  • Raja Iqbal
  • Narayan Ramasubbu

Abstract

Agentic AI systems are increasingly being explored as production infrastructure: they reason over multiple steps, call tools, act through workflows, and adapt through memory and feedback. These systems create governance challenges that are not fully captured by traditional software or predictive ML technical debt. We define Agentic Technical Debt as the accumulated liability created when prompts, memory, tool schemas, orchestration graphs, control policies, and observability routines are patched together faster than they can be validated, standardized, and governed. We define Stochastic Tax as the recurring operating burden of keeping probabilistic agent behavior within acceptable bounds. The distinction matters: debt is a stock of design and governance liability, while the tax is a flow of operating cost that arises because stochastic agents act through tools and workflows. We outline how managers can make both visible through lightweight dashboards and governance controls.

Suggested Citation

  • Muhammad Zia Hydari & Raja Iqbal & Narayan Ramasubbu, 2026. "Governing Technical Debt in Agentic AI Systems," Papers 2605.29129, arXiv.org, revised Jun 2026.
    • Handle: RePEc:arx:papers:2605.29129
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    1. Muhammad Zia Hydari & Raja Iqbal & Narayan Ramasubbu, 2026. "Modeling Agentic Technical Debt and Stochastic Tax: A Standalone Framework for Measurement, Simulation, and Dashboarding," Papers 2605.27320, arXiv.org.
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