提出自适应多维监控算法，提升智能体系统异常检测速度与准确性。
Adaptive Monitoring and Real-World Evaluation of Agentic AI Systems
- 设计自适应多维监控算法，动态调整阈值并融合多指标异常检测。
- 实测将异常检测延迟从12.3秒降至5.6秒，误报率由4.5%降至0.9%。
- 适用于高风险场景的AI系统评估，尤其适合关注安全与可靠性的研究者。
自主人工智能系统正快速从实验室走向高风险应用领域。本文回顾2023–2025年84篇相关论文发现，83%仅报告能力指标，仅30%考虑人本或经济维度。为此，提出自适应多维监控（AMDM）算法：对异构指标进行归一化处理，各维度采用指数加权移动平均阈值，并通过马氏距离实现联合异常检测。在模拟与真实场景中验证，AMDM将目标漂移检测延迟从12.3秒降至5.6秒，误报率由4.5%降至0.9%。提供代码、数据与可复现性清单，支持研究复现。代码见https://github.com/Manishms18/Adaptive-Multi-Dimensional-Monitoring。
原文摘要 · Abstract (English)
Agentic artificial intelligence (AI) -- multi-agent systems that combine large language models with external tools and autonomous planning -- are rapidly transitioning from research laboratories into high-stakes domains. Our earlier "Basic" paper introduced a five-axis framework and proposed preliminary metrics such as goal drift and harm reduction but did not provide an algorithmic instantiation or empirical evidence. This "Advanced" sequel fills that gap. First, we revisit recent benchmarks and industrial deployments to show that technical metrics still dominate evaluations: a systematic review of 84 papers from 2023--2025 found that 83% report capability metrics while only 30% consider human-centred or economic axes [2]. Second, we formalise an Adaptive Multi-Dimensional Monitoring (AMDM) algorithm that normalises heterogeneous metrics, applies per-axis exponentially weighted moving-average thresholds and performs joint anomaly detection via the Mahalanobis distance [7]. Third, we conduct simulations and real-world experiments. AMDM cuts anomaly-detection latency from 12.3 s to 5.6 s on simulated goal drift and reduces false-positive rates from 4.5% to 0.9% compared with static thresholds. We present a comparison table and ROC/PR curves, and we reanalyse case studies to surface missing metrics. Code, data and a reproducibility checklist accompany this paper to facilitate replication. The code supporting this work is available at https://github.com/Manishms18/Adaptive-Multi-Dimensional-Monitoring.
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