# Adaptive Monitoring and Real-World Evaluation of Agentic AI Systems

## TL;DR

This paper introduces an adaptive multi-dimensional monitoring algorithm for agentic AI systems, demonstrating improved anomaly detection and evaluation methods through simulations and real-world experiments.

## Contribution

It formalizes the AMDM algorithm for monitoring agentic AI, addressing the gap in empirical evaluation and providing practical tools for real-world deployment.

## Key findings

- AMDM reduces anomaly detection latency from 12.3s to 5.6s.
- AMDM lowers false-positive rates from 4.5% to 0.9%.
- Systematic review shows focus on capability metrics over human-centred axes.

## Abstract

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.