Author: Denis Avetisyan
A new framework for understanding and mitigating the hidden risks within generative and agentic artificial intelligence.
This review details a layered approach to analyzing AI failure modes and proposes an ‘awareness mapping’ methodology for organizational preparedness, emphasizing systemic dependability.
Despite growing reliance on generative and agentic AI, systemic vulnerabilities remain poorly understood and often propagate across technological layers. This paper, ‘From Failure Modes to Reliability Awareness in Generative and Agentic AI System’, addresses this gap by introducing an 11-layer failure stack and a corresponding ‘awareness mapping’ methodology to quantify organizational preparedness for managing AI risks. The core finding demonstrates that proactively assessing reliability awareness—not just identifying failure points—is crucial for building trustworthy and sustainable AI deployments. How can this framework be effectively integrated into asset management strategies to ensure long-term resilience in mission-critical applications?
The Architecture of Failure
As AI systems evolve—encompassing Generative AI, Agentic AI, and beyond—ensuring consistent and safe performance, termed AI Reliability, is paramount. Traditional quality assurance struggles with the emergent behaviors of these sophisticated systems; static analysis and predefined tests are insufficient when dealing with models capable of novel outputs. Failures are no longer isolated; they propagate, potentially leading to systemic instability. The interconnectedness within these architectures necessitates holistic reliability assessments—a controlled unraveling, if you will.
Deconstructing the Machine
The increasing complexity of modern AI necessitates a comprehensive framework for identifying vulnerabilities, extending beyond software flaws to encompass hardware failures and execution environment issues. The 11-Layer Failure Stack provides a structured methodology, utilizing 47 diagnostic points to pinpoint weaknesses across all layers. This granular analysis maps connections between seemingly disparate issues—data quality, network latency—to offer a holistic understanding of system resilience and potential catastrophic failures.
Mapping the Blind Spots
Awareness Mapping evaluates an organization’s understanding of AI reliability risks and maturity, establishing a baseline for improvement. Initial assessments reveal a prevalent lack of in-depth understanding; most organizations operate at Level I or Level II—’Unaware’ or ‘Fragmented Awareness’—demonstrating limited recognition of risks beyond superficial system behavior. Effective AI Governance—defined policies, standardized procedures—is essential to translate awareness into actionable risk mitigation, preventing unpredictable failures.
Beyond Predictability
The pursuit of AI reliability extends beyond failure prevention, concerning the establishment of trust and responsible innovation. Ensuring predictable and beneficial operation necessitates a shift from benchmark accuracy to holistic evaluation under diverse and adversarial conditions. Addressing hallucinations, goal misalignment, and emergent behaviors is critical for aligning AI with human values. Prioritizing robustness—maintaining performance despite corrupted inputs or shifts—will unlock AI’s full potential; perhaps the glitch isn’t a breakdown, but a glimpse into the machine’s attempt to define its own reality.
The pursuit of AI reliability, as detailed in the paper’s layered framework, mirrors a fundamental principle of understanding any complex system: deconstruction to reveal underlying weaknesses. This echoes the sentiment of Epicurus, who stated, “It is impossible to live pleasantly without living prudently and honorably.” The paper advocates for an ‘awareness mapping’ methodology – a systematic probing of potential failure modes. This isn’t about predicting disaster, but about proactively dismantling assumptions and identifying vulnerabilities across layers, much like a hacker reverse-engineers a system not to destroy it, but to fully comprehend its architecture. The framework’s emphasis on cross-layer risks demonstrates that true dependability isn’t achieved through isolated fixes, but a holistic understanding of interconnectedness.
What’s Next?
The presented framework, while a step toward systematizing AI dependability, fundamentally reveals how little of the code—the underlying logic of these systems—is truly understood. Awareness mapping, as detailed, isn’t about preventing failures, but rather cataloging the inevitable points of breakage. It’s a process of reverse-engineering emergent behavior, documenting the ways in which these systems deviate from intention. The true challenge isn’t building more robust AI, but developing the diagnostic tools to rapidly disassemble and understand what went wrong when—and why the failure was, in some sense, predictable.
Current approaches largely treat AI as a black box, focusing on outputs rather than internal states. Future work must prioritize interpretability—not as an ethical concern, but as a necessary engineering prerequisite. Dependability-Centred Asset Management, applied to AI, requires a granular understanding of component interactions, and the ability to trace failures back to specific lines of code, or, more likely, to the probabilistic weights within vast neural networks. This demands a shift from performance metrics to failure mode analysis as the primary design driver.
Ultimately, the field needs to acknowledge reality is open source—the principles governing intelligence are inherent in the universe; it is simply a matter of time—and diligent probing—before the code is fully read. The presented work suggests that ‘reliability’ isn’t a destination, but an ongoing process of meticulous deconstruction and reconstruction. The next iteration won’t be about building ‘safe’ AI, but about building AI that fails interestingly—and from which something genuinely novel can be learned.
Original article: https://arxiv.org/pdf/2511.05511.pdf
Contact the author: https://www.linkedin.com/in/avetisyan/
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2025-11-11 22:41