As artificial intelligence systems increasingly transition from controlled laboratory environments to real-world deployment, their ability to handle unexpected failures becomes a critical determinant of practical utility and safety. This paper introduces a comprehensive framework for failure-aware artificial intelligence, encompassing systematic mechanisms for detecting, categorizing, and responding to failures in deployed AI systems. We propose a three-tier failure taxonomy that distinguishes between input-level anomalies, processing-level errors, and output-level inconsistencies, each requiring distinct detection and recovery strategies. The proposed architecture integrates continuous self-monitoring components, confidence estimation modules, and adaptive recovery mechanisms that enable graceful degradation rather than catastrophic failure. Building upon prior work in modular robotic system architectures and patented approaches to dexterous task execution, we present design principles for building failure-resilient AI systems, including redundancy patterns, fallback hierarchies, and human-in-the-loop escalation protocols. Evaluation through simulated failure injection across multiple AI task domains demonstrates that failure-aware systems maintain operational continuity in 87% of induced failure scenarios, compared to 23% for conventional architectures. The framework provides practitioners with actionable guidelines for enhancing the robustness and reliability of deployed artificial intelligence systems across diverse application contexts.