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Turns Out AI Agents Aren't Mostly AI

A waiter serving diners at a restaurant

Recently, I've been playing around with cognitive architecture design. Don't worry, I'll dive into it in just a bit. But what I have found is that they make building AI agents easier because you have to actively invest in planning how data flows in and out, as well as the agent's decision-making, reasoning, grounding, and memory.

Still, For All Mankind

Apollo 11 plaque left behind on the Moon

Over the last year, demand for intelligence has skyrocketed. Agentic AI is more popular than ever. However, the popularity of the infrastructure necessary for its acceleration is declining rapidly. Everybody hates datacenters. New York moved to block the construction of new hyperscale data centres, but Starcloud is here to save the day. Or is it?

Who's watching the Watchmen?

Rorschach from Watchmen, in falling snow

Flock Safety's cameras have helped solve over a million crimes. Its audit tool just caught nine cops abusing the system in one day. Both of those facts are true, and neither one settles the actual question: who decides how much surveillance is worth how much safety and who gets to check the checker?

The Seven Gates We Put Between an AI Agent and Our dbt Models

How our data team stopped trusting a coding agent to remember the process.

TL;DR: An AI agent will write you a dbt model that compiles, passes every test, and is wrong. Adding more instructions doesn't fix this, because the real gap is memory, not knowledge. We moved the process out of the agent's head and into the repo: seven phases, each a gate that must be verifiably passed before the next can start. Below is the whole workflow, what triggered it, and what it's caught so far.

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