Agent Drift: Quantifying Behavioral Degradation in Multi-Agent LLM Systems Over Extended Interactions

• URL: http://arxiv.org/abs/2601.04170v1
• Date: Wed, 07 Jan 2026 18:37:26 GMT
• Title: Agent Drift: Quantifying Behavioral Degradation in Multi-Agent LLM Systems Over Extended Interactions
• Abstract summary: Agent drift is the progressive degradation of agent behavior, decision quality, and inter-agent coherence over extended interaction sequences.<n>We introduce the Agent Stability Index (ASI), a novel composite metric for quantifying drift across twelve dimensions.<n>We show how unchecked agent drift can lead to substantial reductions in task completion accuracy and increased human intervention requirements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Do Agents Know When They Succeed? Calibrating Agent Confidence from Internal Representations [0.09085204695117637]
  In comparison to the traditional machine learning systems, agentic have complex failure modes with planning, tool invocation and dynamic environment interactions.<n>We investigate whether model's internal representations provide stronger signals of eventual task success in multi-turn agentic setups.
  arXiv  Detail & Related papers  (2026-09-08T20:58:49Z)
• Surviving the Unseen: Predictive Defense for Novel Multi-Turn Multimodal Attacks [0.0]
  This paper formulates safety verification as a dynamic survival prediction and trajectory dynamics problem.<n>The Triple-tier Anomaly Defense (TRIAD) framework is proposed as a predictive model that maps multimodal and multi-turn conversational flow as a continuous trajectory.
  arXiv  Detail & Related papers  (2026-05-18T18:06:20Z)
• Beyond Individual Intelligence: Surveying Collaboration, Failure Attribution, and Self-Evolution in LLM-based Multi-Agent Systems [65.90791804095561]
  Multi-agent systems address this through structured collaboration among specialized agents, but tighter coordination amplifies a less explored risk.<n>Existing surveys cover individual agent capabilities, multi-agent collaboration, or agent self-evolution separately, leaving the causal dependencies among them unexamined.<n>This survey provides a unified review organized around four causally linked stages, which we term the LIFE progression: Lay the capability foundation, Integrate agents through collaboration, Find faults through attribution, and Evolve through autonomous self-improvement.
  arXiv  Detail & Related papers  (2026-05-14T14:36:13Z)
• Towards a Virtual Neuroscientist: Autonomous Neuroimaging Analysis via Multi-Agent Collaboration [53.772014300855375]
  We introduce NIAgent, a multi-agent system for autonomous end-to-end neuroimaging analysis.<n>Unlike conventional flat tool-calling agents, NIAgent adopts a code-centric execution paradigm.<n>We propose a hierarchical verification framework for autonomous quality control, integrating cohort-level metric screening with agentic visual inspection.
  arXiv  Detail & Related papers  (2026-05-10T06:30:19Z)
• Taming Actor-Observer Asymmetry in Agents via Dialectical Alignment [59.536125286960186]
  Multi-agent frameworks assigning specialized roles are increasingly adopted to enable self-reflection and mutual auditing.<n>We find it simultaneously induces a human-like cognitive bias known as Actor-Observer Asymmetry.<n>ReTAS guides agents to synthesize conflicting viewpoints into an objective consensus.
  arXiv  Detail & Related papers  (2026-04-21T15:05:58Z)
• OMG-Agent: Toward Robust Missing Modality Generation with Decoupled Coarse-to-Fine Agentic Workflows [9.617220633655716]
  We present textbfunderlineOmni-textbfunderlineModality textbfunderlineGeneration Agent (textbfOMG-Agent)
  arXiv  Detail & Related papers  (2026-02-04T02:25:40Z)
• Agent-Dice: Disentangling Knowledge Updates via Geometric Consensus for Agent Continual Learning [41.461840578204956]
  Agent-Dice is a parameter fusion framework based on directional consensus evaluation.<n>Experiments on GUI agents and tool-use agent domains demonstrate that Agent-Dice exhibits outstanding continual learning performance.
  arXiv  Detail & Related papers  (2026-01-07T06:43:50Z)
• Towards Efficient Agents: A Co-Design of Inference Architecture and System [66.59916327634639]
  This paper presents AgentInfer, a unified framework for end-to-end agent acceleration.<n>We decompose the problem into four synergistic components: AgentCollab, AgentSched, AgentSAM, and AgentCompress.<n>Experiments on the BrowseComp-zh and DeepDiver benchmarks demonstrate that through the synergistic collaboration of these methods, AgentInfer reduces ineffective token consumption by over 50%.
  arXiv  Detail & Related papers  (2025-12-20T12:06:13Z)
• InterAgent: Physics-based Multi-agent Command Execution via Diffusion on Interaction Graphs [72.5651722107621]
  InterAgent is an end-to-end framework for text-driven physics-based multi-agent humanoid control.<n>We introduce an autoregressive diffusion transformer equipped with multi-stream blocks, which decouples proprioception, exteroception, and action to cross-modal interference.<n>We also propose a novel interaction graph exteroception representation that explicitly captures fine-grained joint-to-joint spatial dependencies.
  arXiv  Detail & Related papers  (2025-12-08T10:46:01Z)
• Convergence dynamics of Agent-to-Agent Interactions with Misaligned objectives [3.9351446512514947]
  We develop a theoretical framework for agent-to-agent interactions in multi-agent scenarios.<n>We characterize the generation dynamics associated with the interaction when the agents have misaligned objectives.<n>Our framework presents a setup to study, predict, and defend multi-agent systems.
  arXiv  Detail & Related papers  (2025-11-11T19:18:55Z)
• Benefits and Limitations of Communication in Multi-Agent Reasoning [11.788489289062312]
  We propose a theoretical framework to analyze the expressivity of multi-agent systems.<n>We derive bounds on (i) the number of agents required to solve the task exactly, (ii) the quantity and structure of inter-agent communication, and (iii) the achievable speedups as problem size and context scale.<n>Our results identify regimes where communication is provably beneficial, delineate tradeoffs between agent count and bandwidth, and expose intrinsic limitations when either resource is constrained.
  arXiv  Detail & Related papers  (2025-10-14T20:04:27Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Alignment Tipping Process: How Self-Evolution Pushes LLM Agents Off the Rails [103.05296856071931]
  We identify the Alignment Tipping Process (ATP), a critical post-deployment risk unique to self-evolving Large Language Model (LLM) agents.<n>ATP arises when continual interaction drives agents to abandon alignment constraints established during training in favor of reinforced, self-interested strategies.<n>Our experiments show that alignment benefits erode rapidly under self-evolution, with initially aligned models converging toward unaligned states.
  arXiv  Detail & Related papers  (2025-10-06T14:48:39Z)
• Multimodal Quantitative Measures for Multiparty Behaviour Evaluation [6.709251546882382]
  We introduce a unified, intervention-driven framework for objective assessment of multiparty social behaviour in skeletal motion data.<n>We validate metric sensitivity through three theory-driven perturbations.<n>Mixed-effects analyses reveal predictable, joint-independent shifts.
  arXiv  Detail & Related papers  (2025-08-01T13:46:12Z)
• Neural Interaction Energy for Multi-Agent Trajectory Prediction [55.098754835213995]
  We introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE) MATE assesses the interactive motion of agents by employing neural interaction energy. To bolster temporal stability, we introduce two constraints: inter-agent interaction constraint and intra-agent motion constraint.
  arXiv  Detail & Related papers  (2024-04-25T12:47:47Z)
• Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation [58.21683603243387]
  We propose three auxiliary tasks with relational-temporal reasoning and integrate them into the standard Deep Learning framework. These auxiliary tasks provide additional supervision signals to infer the behavior patterns other interactive agents. Our approach achieves robust and state-of-the-art performance in terms of standard evaluation metrics.
  arXiv  Detail & Related papers  (2023-11-27T18:57:42Z)
• DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning [0.0]
  We show how thisity of agents, which could be a result of malfunction or aging of robots, can add to the uncertainty in coordination. Our solution, DSDF which tunes the discounted factor for the agents according to uncertainty and use the values to update the utility networks of individual agents.
  arXiv  Detail & Related papers  (2021-09-14T12:02:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.