Tag: system-reliability
AI errors need AI-level explanations
AI systems fail gradually and partially, not in clear binary states like traditional software. Most AI proof-of-concepts never reach production. The model gives a plausible answer missing important context, latency spikes but stays under timeout limits, outputs degrade invisibly. Your AI error handling must match this reality.
Multi-model AI strategies - why diversity is your safety net
When ChatGPT went down for 12 hours in June 2025, thousands of businesses had no fallback. Multi-model routing is fast becoming standard for serious AI deployments. Task-specific routing can cut inference costs by up to 85 percent. Resilience through model diversity is not optional.
AI incident response: Why most incidents are process failures
The most damaging AI incidents stem from process breakdowns, not technical failures. The AI Incident Database reached 1000 incidents by 2025, with GenAI involved in 70% of cases. Building incident response that addresses process causes rather than just technical symptoms is what prevents repeat failure.