Most AI projects never make it past the prototype stage because we treat them as magic instead of software. Moving from a "cool demo" to a production-grade, Cloud Native system requires the same architectural rigor we've spent decades building, while asking the same questions with a healthy level of skepticism, with new rules for non-deterministic code.

This talk will look at how AWS, GCP, and Azure have codified AI engineering, breaking down the patterns that actually work: from safe rollout strategies like shadow deployments to the shift from classic RAG to autonomous multi-agent loops. At the end we'll have a blueprint to work with that doesn't just "work on my machine", but scales, remains observable, while staying cost-effective in the real world.