One signal

A new practitioner’s guide argues that production failures in AI agents and retrieval-augmented generation often start with the data foundation, not the model. It points to gaps including real-time synchronisation, structured document parsing and governed semantic layers.

The guide also names operational context stores, zero-trust security and automated curation as parts of a stronger enterprise AI foundation. Those are useful clues for teams trying to make AI systems work reliably beyond a demo, though the supplied account does not explain the six patterns in detail. That is a useful reminder that better prompts cannot fix every plumbing problem.

Discuss: Should teams prioritise stronger data foundations before scaling AI features, even if that slows deployment?

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