Most organizations approaching AI agents ask which model to use. It is the wrong first question, and the answer keeps changing anyway. Here is the more useful framing, which I picked up from Eran Yahav of Tabnine and have not been able to unsee since. An enterprise agent stack has three parts, not one: The LLM — powerful, general, and completely ignorant of your organization. It has never seen your systems, your history, your incidents, or the reason that one service is named after someone's dog. The agent — orchestration, tool use, interaction with humans. The context engine — a persistent, continuously maintained map of the organization itself. Almost everyone invests in the first two and improvises the third. That is why agents fail most complex enterprise tasks — and the failures are usually not reasoning failures. They are onboarding failures. The agent lacks the knowledge a human engineer accumulates in their first few months and then never thinks about again. The bl...
One idea per post, distilled from a book a week, articles and the podcasts I follow — with the flashcards I use to remember it