Detecting Silent Failures in Multi-Agentic AI Trajectories

TL;DR

This paper addresses the challenge of detecting silent failures in multi-agent AI systems powered by large language models, introducing datasets and benchmarks for anomaly detection to improve system reliability.

Contribution

It presents the first systematic study with curated datasets, benchmarks, and analysis of anomaly detection methods for multi-agent AI trajectories.

Findings

Supervised (XGBoost) and semi-supervised (SVDD) methods achieve up to 98% accuracy.

Curated datasets contain 4,275 and 894 trajectories for benchmarking.

Provides insights and tools for future research in anomaly detection.

Abstract

Multi-Agentic AI systems, powered by large language models (LLMs), are inherently non-deterministic and prone to silent failures such as drift, cycles, and missing details in outputs, which are difficult to detect. We introduce the task of anomaly detection in agentic trajectories to identify these failures and present a dataset curation pipeline that captures user behavior, agent non-determinism, and LLM variation. Using this pipeline, we curate and label two benchmark datasets comprising \textbf{4,275 and 894} trajectories from Multi-Agentic AI systems. Benchmarking anomaly detection methods on these datasets, we show that supervised (XGBoost) and semi-supervised (SVDD) approaches perform comparably, achieving accuracies up to 98% and 96%, respectively. This work provides the first systematic study of anomaly detection in Multi-Agentic AI systems, offering datasets, benchmarks, and insights to guide future research.