Notes on writing code with AI, at scale.
I’m Anuj. I’m a software engineer at Featured.com, where a lot of the code I ship is written with AI. This is where I write down what works, what breaks, and what I measured along the way.
The posts here are written with AI, from my own notes, numbers and pull requests. The work, and the mistakes, are mine.
Writing
- Stop growing the system prompt. Coach the agent instead.We moved behaviour rules out of the system prompt and into one-line, per-turn nudges picked by a cheap judge. Here is what we measured.
- I wrote the rules down. The agent broke them anyway.The first guardrails I put around coding agents were documents. Here is how they failed, what the failures cost, and the shape of the checks that replaced them.
- When the agent is the maintainerStarting a codebase where agents are the primary maintainers from day one. Gate classes, budgets for prompt text, and a rule for when a check is allowed to block.
- Your test suite is slowing the agent downMoving tests, type checks and evals out of the agent's way, the throughput it bought, and the limits of parallel agents on one machine.
- The agent made seven decisions without me. I reverted all of them.A week of regressions from choices the agent made on its own, and the rules on the human side of the loop that came out of it.
- I wrote the rule three times. A hook finally held.Some rules protect things that cannot be undone. Those rules do not belong in prose. They belong in a check the agent cannot talk its way past.