This paper proposes a managerial measurement framework for agentic AI systems. Its central idea is that organizations should separate two related but different costs: Agentic Technical Debt is a stock: accumulated design and governance liability caused by shortcuts in prompts, tools, memory, orchestration, observability, platform coupling, and control processes. Stochastic Tax is a flow: the recurring operating burden of using probabilistic agents in real workflows, including evaluation, monitoring, retries, escalations, revalidation, latency, token/context cost, and security/guardrail maintenance. Importantly, this tax can remain positive even if technical debt is minimized, because stochastic systems still vary across runs, depend on tools and context, and encounter new edge cases. The paper is not mainly about improving model accuracy or proposing a new agent architecture. It is about how to measure, budget, simulate, and govern the operational cost of agentic AI systems. Main contributions 1. It introduces a useful stock-flow distinction for agentic AI governance The strongest contribution is conceptual: Agentic Technical Debt is a stock; Stochastic Tax is a flow. This prevents managers from making a common mistake: assuming that all agent operating cost is caused by bad design. Some costs are avoidable debt-amplified costs, but some are baseline costs of operating stochastic agents safely. 2. It expands technical debt from software/ML debt to agentic-system debt The paper extends technical debt beyond code, data, and ML pipelines into agent-specific surfaces: prompts, context, tools, schemas, memory, routing, observability, governance routines, and platform coupling. This is useful because these are exactly the places where real agentic systems become hard to change, test, explain, and control. 3. It gives a formal but dashboard-friendly model The framework is mathematically simple enough to implement in a spreadsheet, but structured enough to distinguish debt, usage, surface area, autonomy, workflow horizon, and model variability. This makes it more useful for management than a vague “AI ops cost” discussion. 4. It provides a measurable cost taxonomy The eight stochastic-tax categories—evaluation, monitoring, retry/repair, escalation, revalidation, latency, token/context/compute, and security/guardrails—give teams a practical way to instrument agent operations. The paper also links each category to measurement rules and common pitfalls, such as ignoring tail latency, treating guardrails as one-time implementation, or counting retries while ignoring self-repair token consumption. 5. It connects governance decisions to unit economics The model lets a team ask: “Is cost rising because we scaled responsibly, because the workflow became more autonomous, because model variability increased, or because technical debt accumulated?” That is a useful management decomposition. The dashboard design tracks total tax, per-transaction tax, baseline tax, debt-amplified tax, debt components, driver indicators, and calibration status. 6. It offers an implementation path The paper gives a seven-step implementation process: define workflow boundaries, score debt components, collect operating signals, convert signals to dollars, calibrate parameters, estimate baseline tax with uncertainty, and use the decomposition for decisions. This makes the paper closer to an operating framework than a purely theoretical note.