AI agents have the potential to revolutionize business operations, but many organizations are finding that their AI agents are not meeting expectations due to various failures such as inconsistent responses, unexpected behaviors, and system breakdowns. These failures can be attributed to several factors including hallucination and factual inaccuracy, context window limitations, prompt injection and security vulnerabilities, inadequate training data coverage, poor error handling, inconsistent output formatting, and latency and performance bottlenecks. To address these issues, it is essential to understand the intricate web of factors that contribute to agent reliability and to implement systematic approaches to prevention, detection, and mitigation. This involves evaluating generative AI systems thoroughly, building robust defenses against security threats, designing systems that can handle uncertainty and ambiguity, and implementing comprehensive monitoring and evaluation frameworks to detect and respond to failures quickly. By taking a proactive approach to addressing these challenges, organizations can build reliable AI agents that are stable, safe, and production-ready, ultimately transforming experimental prototypes into trusted systems that deliver business value.