The part after launch is the part you are paying for
Models drift, prompts rot, and upstream APIs change their output shape without telling you. What a maintenance loop for an AI system actually contains.
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An AI feature is not finished when it is deployed. That sentence is easy to agree with and almost nobody budgets for it, which is why so many pipelines that were accurate in month one are quietly wrong by month four.
Three things that change without anyone touching the code
- The provider updates the model behind the version string you pinned, or deprecates it entirely.
- An upstream API adds a field, reorders a list, or starts returning
nullwhere it used to return an empty array. - Your own data changes shape, because the business changed and nobody thought to mention it to the pipeline.
What a maintenance loop contains
An evaluation set that reflects real traffic rather than the examples used during the build. A scheduled run of it. An alert when a score moves, and a human who reads that alert. None of this is sophisticated; the reason it is rare is that it produces no demo.
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