Failure Modes in Agentic AI Systems: A Framework for Graceful Degradation and Human Handoff Design
Year: 2026
Type: article
Abstract: This industry white paper explores the failure modes of production AI agents across enterprise environments, including HRTech, HealthTech, logistics, and customer operations. Drawing on primary research from RAND (2024), McKinsey (2025), Gartner (2025), PwC (2025), S&P Global (2025), and Maxim AI (2025), the paper identifies six core failure modes that account for the majority of AI deployment breakdowns, including scope misalignment, lack of observability, data drift, and confidence miscalibration. The paper proposes a three-layer resilience framework for production-grade AI systems:1. Graceful Degradation2. Observable Behaviour3. Human Handoff Design It argues that AI agent reliability is not a model problem, but a system design problem. Organisations that invest in failure design build AI systems that are more resilient, transparent, and trustworthy. This work is intended for CTOs, AI engineers, product leaders, and enterprise decision-makers building or deploying agentic AI systems in production.
Author: Saxena, Diwesh
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7151587784
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