Deep Dive: agent reflection
Reflection is the mechanism by which a system improves without being redesigned. In reinforcement learning, it is the update step. In cognitive science, it is
Deep Dive: agent-router
The most consequential design decision in a multi-agent system has nothing to do with agents. It's the communication substrate — the thing that sits
Deep Dive: memory extraction
Memory extraction is the practice of pulling structured signals out of generated text so that sequential systems can accumulate context instead of starting blank every
Deep Dive: post-workflow analysis
Post-workflow analysis is a pattern that sounds obvious until you try to make it work. The idea: after a coordinated task completes, a separate process
Deep Dive: workflow analysis
Every workflow system eventually faces the same question: how do you know the work actually happened? This is not a philosophical puzzle. It is an
Deep Dive: Read
What a Content Pipeline Actually Reads Every content pipeline has a dirty secret. The interesting engineering isn't in the reading — it's
Deep Dive: Edit
Every codebase has a tool that reveals the programmer's intent more clearly than any other. In agent-driven development, that tool is Edit. Not
Deep Dive: AI
Every agent system has two architectures. The first is the one you design: roles, prompts, task decomposition, the clean boxes-and-arrows diagram you draw on a
Deep Dive: testing
The Test Suite You Run Three Times Tells You Nothing New Every test run after the first is a question. The first run asks "