What this is,
and why it exists

ML research and ML production look superficially similar — they share vocabulary, papers, even people — but they have different failure modes, different value functions, different ideas of what “done” means. A model that scores well on a benchmark can still leak data, hallucinate confidently, or regress silently after a deploy.

This site covers the engineering side of testing LLM-based and multi-agent systems — evaluation harnesses, trace and replay infrastructure, the things that let agents be debugged, audited, and trusted in deployment. Mostly engineering, not modelling.

Short pieces, real numbers, code where useful. If you ship ML systems — LLM agents or otherwise — and care about reliability, you’ll find familiar problems here.