Fault Tolerance and Graceful Degradation

AI systems rely on flaky, slow dependencies (model providers, vector stores, tools), so they must degrade gracefully rather than fail hard. Circuit breakers stop calling a failing dependency so it can recover; fallbacks return a cached, simpler, or safe response when the primary path fails; timeouts and bulkheads keep failures contained. The aim is for one component's failure to become a degraded experience, not an outage. AI, ML, and GenAI engineer interviews probe it because LLM dependencies fail often and naive designs turn a provider blip into a total outage.