Most AI projects fail not because the models are wrong, but because the engineering around them is underprepared for production realities. This article dissects the real architectural failure patterns in LLM, RAG, and compound AI systems — covering retrieval degradation, silent model failures, cost explosions, cascade failures, and the observability gaps that make them invisible — and provides the production-grade architectural patterns that fix them.