Issue 18, Oct 7, 2026

The Harness Overfitting Trap

Senior AI Engineer interview at Google DeepMind, and the interviewer asks:

“You fine-tuned an agent on 50K trajectories, all collected through one agent harness. A customer runs it in a different harness on the same kind of task, and performance collapses. Same weights, same task. What did your model actually learn?”

Don’t say: “The new harness has a different system prompt. Tweak the prompt, add a few examples, maybe collect more data.”

Why SFT quietly memorizes framework dialects instead of tasks, and how frontier labs use harness-neutral pipelines to stop catastrophic off-distribution collapse.

Share this trapShare on LinkedIn

The full answer, with the mechanism and the arithmetic, is for paid subscribers on Substack.

Read it on Substack

Get the next one

Free on Substack. Unsubscribe in one click.

More traps set at Google DeepMind