From Failure Modes to Reliability Awareness in Generative and Agentic AI System
This work addresses reliability issues for organizations deploying AI systems, but it is incremental as it builds on existing frameworks like Dependability-Centred Asset Management.
The paper tackles the problem of reliability in generative and agentic AI systems by introducing an 11-layer failure stack to identify vulnerabilities and a maturity-oriented awareness mapping framework to quantify risk recognition, positioning it as a tool for trustworthy AI deployment in mission-critical domains.
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.