# Detecting Silent Failures in Multi-Agentic AI Trajectories

arXiv: https://arxiv.org/abs/2511.04032 · HTML: https://mlsanity.com/paper/2511.04032/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 6 of its 19 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 6 of 6)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [Multi-Agent Risks from Advanced AI](https://mlsanity.com/paper/2502.14143/) (2025)
- [SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems](https://mlsanity.com/paper/2505.24201/) (2025)

## Built on by (most cited first, 8 of 8)

- [Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems](https://mlsanity.com/paper/2604.14228/) (2026)
- [ContextBench: A Benchmark for Context Retrieval in Coding Agents](https://mlsanity.com/paper/2602.05892/) (2026)
- [VerifyMAS: Hypothesis Verification for Failure Attribution in LLM Multi-Agent Systems](https://mlsanity.com/paper/2605.17467/) (2026)
- [What Resolve Rate Hides: Trajectory Structure Diagnostics for Coding Agents](https://mlsanity.com/paper/2607.06184/) (2026)
- [Content-Aware Attack Detection in LLM Agent Tool-Call Traffic: An Empirical Study of Features, Architectures, and Evaluation Protocols](https://mlsanity.com/paper/2605.11053/) (2026)
- [DAAF: From Failure Localization to Editable System Assets in LLM Agents](https://mlsanity.com/paper/2609.32498/) (2026)
- [Maat: Independent Deterministic Contract-Based Governance for Multi-Agent LLM Workflows](https://mlsanity.com/paper/2609.34017/) (2026)
- [MAADBench: The Refreshable Paradigm for Anomaly Detection in Multi-Agent Systems](https://mlsanity.com/paper/2609.36556/) (2026)

## Abstract (arXiv)

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 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.