It Works! Now What? Fast Iteration for AI Capabilities in Airflow

Building an AI capability in Airflow is the easy part. The hard part is what comes next.

You want to swap a model, refactor a prompt, cut token costs, or try a local model instead of paying for cloud. How do you know it still works as expected? Without a fast feedback loop, every change is a gamble.

This talk shows practical patterns for building that feedback loop, with real examples using agent skills, MCPs, and local and cloud models. It covers the challenges too: sandboxing, observability, non-determinism, and keeping checks simple enough that people actually use them.