Fault Tolerance and Graceful Degradation

AI systems depend 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 contain failures. The goal is that one component's failure becomes a degraded experience, not an outage. Applied-AI interviews probe it because LLM dependencies fail often and naive designs turn a provider blip into a total outage.