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Practical notes on AI that has to work.

What we learn building AI systems, enterprise software and cloud platforms for European organisations: where people stay in control, how to make answers checkable, and what regulation asks of you.

Written for decision makers and engineers across Europe, with sources you can check.

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3 articles
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AI agents · Public sector · AI engineering · Data

The high-risk rules moved to December 2027, but prohibitions, AI literacy and transparency duties already apply. What changed in 2026, what a public deployer has to do, and a practical plan for the time that is left.

FromNine editorial team7 min read

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  1. AI agents / Public sector

    Where AI agents need human approval

    An agent can prepare almost anything. The design question is which actions it may complete on its own, and which need a person to say yes. A practical way to place approval gates, and to move them as evidence grows.

    FromNine editorial team7 min read

  2. AI engineering / Data

    What makes enterprise knowledge search trustworthy

    Answering questions from your own documents is easy to demonstrate and hard to trust. Trust comes from five properties you can design, test and keep testing: permissions, provenance, freshness, honest gaps and measured quality.

    FromNine editorial team6 min read