Why AI Tool Chains Break in Production (And the Patterns That Actually Hold Up) (opens in new tab)

There is a specific moment most developers hit when building AI chains, usually somewhere around the third or fourth iteration: the demo works perfectly, the test runs fine, and then something in production produces output that is technically valid but structurally wrong in a way that breaks everything downstream. The chain was brittle. You just did not know it yet. What follows is a collection of failure patterns that come up repeatedly when developers actually ship AI automation — not the p...