Multi-agent Large Language Model (LLM) systems have emerged as powerful architectures for complex task decomposition and collaborative problem-solving. However, their long-term behavioral stability remains largely unexamined. This study introduces the concept of agent drift, defined as the progressive degradation of agent behavior, decision quality, and inter-agent coherence over extended interaction sequences. We present a comprehensive theoretical framework for understanding drift phenomena, proposing three distinct manifestations: semantic drift (progressive deviation from original intent), coordination drift (breakdown in multi-agent consensus mechanisms), and behavioral drift (emergence of unintended strategies). We introduce the Agent Stability Index (ASI), a novel composite metric framework for quantifying drift across twelve dimensions, including response consistency, tool usage patterns, reasoning pathway stability, and inter-agent agreement rates. Through simulation-based analysis and theoretical modeling, we demonstrate how unchecked agent drift can lead to substantial reductions in task completion accuracy and increased human intervention requirements. We propose three mitigation strategies: episodic memory consolidation, drift-aware routing protocols, and adaptive behavioral anchoring. Theoretical analysis suggests these approaches can significantly reduce drift-related errors while maintaining system throughput. This work establishes a foundational methodology for monitoring, measuring, and mitigating agent drift in production agentic AI systems, with direct implications for enterprise deployment reliability and AI safety research.

翻译：多智能体大语言模型（LLM）系统已成为复杂任务分解与协同问题求解的强大架构。然而，其长期行为稳定性在很大程度上尚未得到充分研究。本研究提出智能体漂移的概念，将其定义为在连续交互序列中智能体行为、决策质量以及智能体间协调性逐渐退化的现象。我们提出了一个理解漂移现象的综合理论框架，提出三种不同的表现形式：语义漂移（逐渐偏离原始意图）、协调漂移（多智能体共识机制失效）以及行为漂移（意外策略的出现）。我们引入了智能体稳定性指数（ASI），这是一个新颖的复合度量框架，用于从十二个维度量化漂移，包括响应一致性、工具使用模式、推理路径稳定性以及智能体间一致率。通过基于仿真的分析和理论建模，我们证明了未受控制的智能体漂移如何导致任务完成准确率显著下降以及人工干预需求增加。我们提出了三种缓解策略：情景记忆巩固、漂移感知路由协议和自适应行为锚定。理论分析表明，这些方法能在维持系统吞吐量的同时，显著减少与漂移相关的错误。本研究为在生产级智能体AI系统中监测、度量和缓解智能体漂移建立了一套基础方法论，对企业部署可靠性和AI安全研究具有直接意义。