Reliability in AI systems is the gap most teams discover only after deployment. The pattern I keep seeing is that teams treat LLM calls like deterministic API calls — no retries, no fallback strategies, no output validation. The most resilient architectures I've worked with treat every LLM response as untrusted input: structured output parsing, confidence thresholds, and graceful degradation when the model returns unexpected formats. Eval-driven development is another underrated practice — having a regression suite for prompt changes is as important as unit tests for code.