Deploy AI Apps On A Speed-Stability Loop
Use DORA-style metrics to improve AI app deployment without turning them into vanity targets.
The goal is not faster shipping. The goal is smaller learning loops with less damage. The deployment loop AI app deployments fail in ways normal web apps do not. The UI can stay up while the assistant gives unsupported answers. Latency can look fine while token cost doubles. A release can pass unit tests while a new retrieval index quietly drops the policy paragraph the model needed. That is why DORA's speed-and-stability frame is useful: it forces teams to look at flow and harm together. Use throughput metrics to see whether change moves in small, frequent batches. Use instability metrics…
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