Documentation often describes how data should look before it reaches an AI agent. Real workflows tell a different story.
Data usually arrives in inconvenient formats, with missing metadata or unexpected structure. Before reasoning begins, developers quietly do a lot of cleanup work.
In practice, preparation looks like this:
Converting files into formats libraries handle well
Removing unnecessary structure
Reducing input size where possible
None of these steps feel innovative, but skipping them almost always causes problems later.
I don’t consider this part of “AI development” anymore. It’s closer to basic hygiene. The goal is to make inputs boring so the agent’s behavior is the interesting part.
As autonomous systems become more common, these real-world preparation habits are being discussed more openly in AI agent communities, including those curated at https://moltbook-ai.com/.
Clean inputs don’t guarantee good outcomes, but messy ones almost guarantee bad ones.
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