Field notes
Notes on building and operating software with AI coding agents under a discipline that makes their output verifiable.
Papers
- Done is a column, not a fact
Every tool we track work in records what a human declared. That was fine when a human did the work. Now the code arrives faster than anyone can read it, and the tracker still takes our word for it.
- The business always wanted a date
Story points were a translation layer between how engineering works and how a business commits. Coding agents broke the layer, and what replaces it is closer to what the business asked for in the first place. The cost shows up somewhere less obvious.
- Team size has a ceiling, and agents lowered it
Coding agents broke the link between complexity and effort, so velocity stopped carrying information. What replaces it is a limit on how many contributors a piece of work can absorb, and the evidence says agents push that limit down rather than up.
- One developer, production scale
What working with AI coding agents actually buys a sole developer running a multi-tenant platform in production, and the three things it does not buy.
- Agents don't fail at writing code. They fail at knowing what already exists.
The hard part of agentic development is not generation. It is knowing what is already in the tree, and nothing about a language model makes it good at that.
- A gate that only ever passes is no gate
Most test suites prove that code works. Almost none prove that the tests would notice if it stopped.