AI demos are optimized for clarity, not chaos. They assume clean inputs, stable formats, and ideal conditions.
Production rarely looks like that.
The first time a demo fails in the real world, it’s often due to something trivial: a file encoded differently, a document structure the parser didn’t expect, or an image format that breaks a dependency.
This isn’t a model problem. It’s a pipeline problem.
I’ve seen teams spend weeks tweaking prompts when the real fix was to normalize inputs earlier. Once data enters the system in a predictable form, agent behavior becomes much easier to debug.
As more autonomous systems move out of demos and into actual use, these issues are becoming more visible. Some AI-focused publications and communities, like https://moltbook-ai.com/
, are starting to highlight the gap between polished demos and messy reality.
The lesson is simple: if a demo only works in perfect conditions, it’s not done yet.
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