Shipping AI You Can Defend: A QA Field Report
Inside the quiet rewiring of data cleanup at Databricks.
The interesting question is not whether Zed works. It does. The interesting question is what teams do with it once the novelty wears off.
Inside Databricks, the rollout looked less like a moonshot and more like a slow migration. A pilot, a champion, a quiet expansion, a budget line.
Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Atlassian's team treats theirs the way an SRE team treats a runbook.
What Alibaba Qwen actually shipped with Command R+ 2 is less a single capability and more a cluster of small, compounding improvements — the kind that only show up when you put a real workflow on top.
Eval harnesses, once an afterthought, are becoming the most important piece of code in many AI projects. Zendesk's team treats theirs the way an SRE team treats a runbook.
Teams that win with small-model orchestration tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
Teams that win with small-model orchestration tend to share a habit: they write the evals before they write the prompts. Everything else follows from that.
None of this guarantees a clean story. Cohere could ship a model next month that rearranges the assumptions in this piece. But the direction of travel, for now, is clear enough to plan around.