When scraping orchestration is the wrong abstraction for LLM workflows

A lot of LLM workflows start with the same small problem: the model needs fresh data from a web page. Then the integration grows sideways. You add a scraper, a queue, a dataset store, polling logic, retries, and a parser. By the end, the code that moves data around is larger than the code that uses the data. This is not because scraping platforms are bad. It is because they solve a broader problem than many LLM apps actually have. The abstraction mismatch Platforms like Apify are bu...