As enterprises adopt AI agents and agentic systems, they discover that these systems fail in unexpected and costly ways. These failures do not follow the patterns of traditional software bugs; they emerge from ambiguity, miscoordination, and unpredictable system dynamics. Fixes that focus only on prompts or tuning fall short. This report introduces a practical framework for understanding agentic failure modes and offers design and oversight strategies to help teams build more reliable, resilient agentic systems.