Designing enterprise AI systems to make humans happier
Nov 9, 2026 - 7 minute read
Most advice about LLM evals is about rigor: calibrated judges and real statistics. All of it is worth doing, and none of it matters if nobody runs the thing. Your eval harness now has two users, a teammate on a deadline and a coding agent looking for the shortest path to green. Build for both of them, make the A/B comparison one command, and the rigor finally gets used.
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Nov 2, 2026 - 5 minute read
A model, a prompt, and a few tools will get you a great demo. Turning that demo into an agent you trust with real work takes more parts, and they’re the same parts every time. There are 22 of them, grouped into eight layers, and every serious agent has some version of each. Once you can see the whole blueprint, you can build the agent you actually want, one deliberate choice at a time.
Oct 23, 2026 - 6 minute read
AI can now write a twelve-page report on anything in under a minute, and nearly every fact in it will be correct. Nobody reads it. The reader skims the headers, forwards it to a teammate, and asks for the short version in a meeting. The best communication in the agent era is a diagram, one sentence that carries the point, and a handful of plain lines that make people believe it.
Oct 19, 2026 - 7 minute read
Every new AI user starts with the same questions. Can it do this? How do I get it to do this? How do I explain what I want? Those are Old Days questions, left over from a world where the person with the idea had to wrangle every tool and every handoff before anything got made. The people getting the most out of AI have quietly stopped asking them, and they now spend their words on what the world should look like when the work is done.
