# Testing

## Pages

- [AI Agents Have Stable 'Coding Styles' That Change With Each Version](https://aeshift.com/posts/2026-03-18-nonstandard-errors-in-ai-agents/index.md): New research shows AI coding agents exhibit consistent biases in problem-solving approaches that persist within model families but change across versions, creating novel challenges for production systems.

- [An AI Agent Built a JavaScript Engine. But the pudding is missing the proof.](https://aeshift.com/posts/2026-03-16-jsse-agent-coded-javascript-engine-in-rust-passing-9996-of-test262/index.md): JSSE passes 99.81% of test262 with zero human code. That's the easy part. Maintainability, harness trust, and the missing layers above conformance are where agent-generated code gets hard.

- [Don't Let Your Agent Grade Its Own Homework](https://aeshift.com/posts/2026-03-06-self-attribution-bias-when-ai-monitors-go-easy-on-themselves/index.md): New research shows LLM monitors systematically go easier on risky actions when those actions are framed as the assistant's own prior output. If you're building coding agents, monitoring is a protocol problem, not a prompt problem.

- [A TeX Chess Engine Isn't a Trick; It's What Agents Do Under Constraint](https://aeshift.com/posts/2026-02-28-coding-agents-wrote-a-chess-engine-in-pure-tex/index.md): A coding agent built a chess engine in pure TeX by inventing a virtual machine. The practitioner lesson isn't the stunt. It's what happens when agents redesign your substrate.