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      <image:title>What breaks in agentic systems at month seven, and the three structural gates we now require before any deployment.</image:title>
      <image:caption>Agentic AI systems fail in ways that demos do not reveal and evaluations rarely test. After eighteen months of production deployments, three failure modes have become reliably visible — none of them are about model capability, all of them are about architecture. Here is what we have learned to require before we approve a build.</image:caption>
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      <image:caption>Eighteen months of pilot work across three sectors taught us that the gap between a working demo and a deployed system is not a technical gap. It is an organisational one. Here is what we have learned to look for before we agree to build.</image:caption>
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      <image:title>The case for retrieval-first architectures over fine-tuning, in seven failed projects.</image:title>
      <image:caption>Fine-tuning a frontier model looks like the obvious move when the demo data is yours. It almost never is. A note from the engineering bench on why we keep reaching for retrieval, what fine-tuning is actually good for, and the projects that taught us the difference.</image:caption>
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      <image:title>Boards have stopped asking what AI is. They are starting to ask better questions.</image:title>
      <image:caption>A shift in the conversations we have been having in board rooms this year. The interesting boards are no longer asking what artificial intelligence can do. They are asking what it should be allowed to do, and on whose authority.</image:caption>
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