At the core of preventing failures in AI agents, especially those used in enterprise settings, is a deep understanding of error propagation, where an initial mistake can cascade into larger system failures. Modern observability platforms reveal systematic failure patterns that can now be detected and prevented at scale. The text highlights seven critical failure modes, including specification and system design failures, reasoning loops and hallucination cascades, context and memory corruption, multi-agent communication failures, tool misuse and function compromise, prompt injection attacks, and verification and termination failures. Each failure mode is described alongside strategies to mitigate them, such as ensemble verification, provenance tracking, standardized protocols, and multi-stage validators. Implementing comprehensive observability and strategic governance, as demonstrated by the Galileo platform, enables teams to transform AI systems into reliable assets that maintain consistency and trustworthiness, even as they scale to handle billions of interactions.