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.

翻译：AI智能体系统正越来越多地被探索作为生产基础设施：它们通过多步推理、调用工具、通过工作流执行，并通过记忆和反馈进行自适应。这些系统所引发的治理挑战超出了传统软件或预测性机器学习技术债务的范畴。我们将智能体技术债务定义为：当提示词、记忆、工具模式、编排图、控制策略和观测流程被快速拼凑，以至于其验证、标准化和治理速度无法跟上时，所累积的负债。我们将随机税定义为：将概率性智能体行为维持在可接受范围内所产生的持续操作负担。两者的区别至关重要：债务是设计和治理负债的存量，而税则是因概率性智能体通过工具和工作流运作而产生的运营成本流。我们概述了管理者如何通过轻量级仪表板和治理控制使这两者可视化。