mlsanityData to 7 Oct 2026

From Failure Modes to Reliability Awareness in Generative and Agentic AI System

arXiv 2511.05511 ↗First posted 24 Oct 2025eess.SY cs.AI cs.SYcited by 0

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

This chapter bridges technical analysis and organizational preparedness by tracing the path from layered failure modes to reliability awareness in generative and agentic AI systems. We first introduce an 11-layer failure stack, a structured framework for identifying vulnerabilities ranging from hardware and power foundations to adaptive learning and agentic reasoning. Building on this, the chapter demonstrates how failures rarely occur in isolation but propagate across layers, creating cascading effects with systemic consequences. To complement this diagnostic lens, we develop the concept of awareness mapping: a maturity-oriented framework that quantifies how well individuals and organizations recognize reliability risks across the AI stack. Awareness is treated not only as a diagnostic score but also as a strategic input for AI governance, guiding improvement and resilience planning. By linking layered failures to awareness levels and further integrating this into Dependability-Centred Asset Management (DCAM), the chapter positions awareness mapping as both a measurement tool and a roadmap for trustworthy and sustainable AI deployment across mission-critical domains.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 3 of its 25 references that are in the map. First posted on arXiv 24 Oct 2025.

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3 of 25 references.

Closest ancestors · 1

Same direction, newest first

  1. 2025Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System12 Aug

Other references · 2

  1. 2024Towards Trustworthy Machine Learning in Production: An Overview of the Robustness in MLOps Approachcited by 3
  2. 2022SOL: Reducing the Maintenance Overhead for Integrating Hardware Support into AI Frameworkscited by 1

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