Nice paper showing how to re-evaluate a production agent at a fraction of the cost.
This is a dev post classified by Jev as Testing & observability (a free resource), kept by the Dev Radar because it carries real work, not commentary.
Nice paper showing how to re-evaluate a production agent at a fraction of the cost. 200 questions, 38.5% of the full benchmark, reproduce the full score to within 1.03 points. The authors studied an analytics agent that serves tens of thousands of monthly users, using 574 historical benchmark runs split by date into calibration and held-out periods. They compared random sampling, cached results, fixed representative subsets and adaptive testing based on item response theory. Multidimensional 2PL adaptive testing gave the best fidelity. The team deployed difficulty-stratified fixed subsets
Posted by elvis (321.1k followers) 2 h ago · 7 likes · 2.3k views · view the original post on X. Kept by the Dev Radar as Testing & observability. Tools mentioned: academy.dair.ai.
More dev work like this
- Browser checks shouldn’t be the part your coding agent skips. — @DanKornas
- Not every problem throws an error. Sometimes checkout just gets slow in one region and… — @sentry
- Tip: learn how to improve your AI model observability using Logs, Activity, and Broadcast. — @OpenRouter
- I just added to my Slow Reader a weekly check that HTTP server and DNS have right… — @sitnikcode
- Excited to ship experiment mocks! — @calcsam
- Next week Bug Bash crosses the Atlantic! — @AntithesisHQ
- It has been a really interesting experience to build an eval suite for System One Models… — @morganlinton
- Testing browser agents on toy sites hides the failures that matter. — @DanKornas
Every post is read and classified by Jev (TypeSafe): what it is, which market it belongs to, and whether the link is a real tool. 23.3k posts from 5k X accounts over the last 21 days, 2.7k tools, 12 markets. Collected every 5 minutes, fully re-ranked every hour — last update 2026-09-24 06:46 UTC. Full method.