From AI Technical Debt to Agentic Technical Debt: A Systematic Mapping of Root Causes and Manifestations in Agentic AI Systems

AI技术债务升级了！这篇论文首次提出“智能体技术债务”概念，专指自主AI系统在协作运行时产生的隐藏问题。就像团队协作会积累沟通成本，AI智能体之间也会因记忆混乱、指令冲突等问题埋下隐患。通俗说，以前的AI技术债务好比单个程序员的代码漏洞，现在智能体AI像一群各怀绝技的“数字员工”，它们自主决策、互相配合，但合作中会产生新麻烦——比如两个AI对同一指令理解不同导致执行混乱，或某个AI“记忆”过时影响整个系统判断。这对普通人的意义在于：未来用AI管家安排行程、AI医生辅助诊断时，系统可能在多环节协作中出现“判断不一致”或“宕机式”失误。论文给出了一套预警框架，相当于给AI团队装上“体检系统”，提前识别沟通不良、协作失灵等风险，避免AI做出危险决策。简单说，这为AI从“单打独斗”到“集团作战”的安全转型提供了基础保障，让智能家居、AI医疗等场景更可靠。

The emergence of Agentic AI systems, characterized by autonomous reasoning, multi-agent collaboration, tool orchestration, adaptive decision-making, and persistent memory, represents a fundamental shift from traditional AI pipelines to dynamic software ecosystems. While AI Technical Debt (AITD) has been widely studied in machine learning and software engineering, existing models assume static, component-level architectures and fail to capture the dynamic and emergent behaviors of agentic environments. To address this gap, this paper introduces Agentic Technical Debt (AgTD), defined as technical debt that emerges, accumulates, propagates, and amplifies due to the autonomous and collaborative nature of Agentic AI systems. Building on our prior systematic scoping review of 31 AITDs across seven root-cause categories, we employ a theory-informed transformation methodology to reinterpret these debts in Agentic AI through direct transformation, contextual transformation, and manifestation expansion. We present the first systematic mapping of established AITDs to their agentic manifestations, showing how conventional debts evolve into system-level liabilities, including memory inconsistencies, orchestration fragility, cascading failures, and unsafe autonomous decision-making. Our findings show that technical debt extends beyond software artifacts to encompass agent behaviors, coordination mechanisms, and interactions among agents, tools, and execution environments. We further examine its implications for AI Trust, Risk, and Security Management (AI TRiSM), highlighting impacts on trustworthiness, governance, security, operational resilience, and Sustainability Technical Debt. Overall, this work establishes AgTD as a foundational software engineering construct and provides a transformation framework, taxonomy, and research agenda for managing technical debt in autonomous multi-agent AI systems.