Running an AI compute layer that sits between automation platforms and upstream LLM providers gives us a unique vantage point on reliability. When OpenAI has a rough day, our customers' workflows need to keep running.
The first lesson is obvious but worth stating: multi-provider is non-negotiable. Any architecture that depends on a single LLM provider will eventually have a bad day. Our routing layer maintains active connections to multiple providers and can redirect traffic in under 100ms.
The second lesson is about queuing. Automation platforms don't always send requests at a steady rate. A Zapier workflow triggered by a marketing email blast might generate 10,000 AI requests in minutes. Our queue system absorbs these bursts and distributes them across providers without dropping requests.
Third: health checks need to be continuous and multi-dimensional. We don't just ping endpoints — we monitor response latency, error rates, output quality, and rate limit proximity across every provider. A provider can be 'up' but degraded, and our routing needs to account for that.
Fourth: graceful degradation beats hard failure. If all premium models are experiencing issues, we can route to alternative models rather than returning errors. An answer from a slightly different model is almost always better than no answer at all.
These principles aren't revolutionary, but implementing them consistently across billions of API calls is where the engineering challenge lives.