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agents

A Tale of Two Particles

Agentic systems have exactly two fundamental particles: text the model reads, and code it can ask to run. One is stochastic, the other deterministic. Almost every confusion about prompts, tools, skills, and agents starts when you lose track of which is which.

The patterns built on top of these two primitives — filtering, skills, progressive disclosure — are all in service of one problem: keeping the stochastic half on the rails as the system grows. This is the problem domain of context engineering. This is the discrete measurement of agent trajectory.

If you aim to replace humans with AI, you're doing it wrong!

Are you betting on replacing your workers' brains (or your own) with AI? You're wrong! There's a better way to approach AI transformation. To explore the ideas behind this claim as it relates to software engineering, I highly recommend Steve Krouse's talk The Role of the Human Brain in Programming (link below). My key takeaway from his talk is that the proper way to think about the human brain in programming isn't that it's replaced by AI, but amplified by it.

AI Software Factory

Obligatory AI generated image of an AI software factory

Local No-Code MCP Agents

Do you keep notes and other configuration files on your local system under a git repo? Do you neglect committing and pushing because it feels like a chore? In this post I introduce one of my favorite new use cases for local, no-code agents using Anthropic's popular MCP standard.