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

TL;DR

This paper introduces a structured 11-layer failure framework and awareness mapping to enhance reliability understanding and management in generative and agentic AI systems, aiming for trustworthy deployment.

Contribution

It presents a novel layered failure stack and awareness mapping framework that links failure propagation to organizational reliability practices in AI systems.

Findings

Failure layers propagate systemically, causing cascading effects.

Awareness mapping quantifies organizational recognition of risks.

Integration with DCAM guides AI reliability improvements.

Abstract

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.