Top Tweets for #SREGym
This post is long overdue, which I owe to the students from the UIUC++ SRSE program. We finally wrote a blog on how we curated a set of high-quality SRE benchmark problems by turning real-world postmortem reports into reproducible SRE problems atop #SREGym. It may give a good example of community-based data curation. Check it out,
https://t.co/vCLXrf87Zk
The fundamental challenge of AI-for-SRE benchmarks is the quality of the problems, e.g., in terms of realism and the ability of challenging frontier AI. This experience enabled us to understand many faces of the challenge and reflect on important tradeoffs between realism, reproducibility, cost, and utility. We learned many lessons, which would guide the effort on building #SREGym 2.0.
Big thanks to @SaadMRP who (unexpectedly) spent his entire summer managing the program with a heroic effort.
I list all the student contributors at the end of the post. Many of them are on Twitter: @12_AbdAllAh_12 @ermiasmulu19 @sharq_fr @M_ABDz_ @MunimThahmid @Sai_Hari_g @ibnAmjid @SuMaya971503 @TalhaAsif25 @tanzimhromel @tejaspkshukla @Varunihk. They did great work to achieve high-quality problems that challenge AI frontier. If you're looking for students or employees, they are good candidates!
Well deserved, @HacksonClark and the #SREGym team! AI for SRE is in an unusual situation, where no strong benchmark is available and the technical landscape is rather opaque. The fundamental challenge is to curate high-quality problems that can reflect real-world characteristics of SRE tasks. It's great to see #SREGym continuously pushing on this direction and offering an open platform for everyone, and more importantly, the active community they have built. The #SREGym Slack has 250+ members and the project has 60+ contributors. The best part is to hear from more and more practitioners who are using it.
I'm excited to share that https://t.co/eDFaNGJ0TF has been accepted to NeurIPS 2026! ๐
Huge thanks to my co-lead @Yiming_Su3, our co-authors @SaadMRP, @lilygn6, Yifan Tian, Hans-Arno Jacobsen, @yinfang_chen , @TianyinXu, and our 60+ contributors!
We have benchmarks for agents that write code.
We built one for what happens after you deploy it.
Can AI resolve production issues? ๐งต [1/N]
![HacksonClark's tweet photo. I'm excited to share that https://t.co/eDFaNGJ0TF has been accepted to NeurIPS 2026! ๐
Huge thanks to my co-lead @Yiming_Su3, our co-authors @SaadMRP, @lilygn6, Yifan Tian, Hans-Arno Jacobsen, @yinfang_chen , @TianyinXu, and our 60+ contributors!
We have benchmarks for agents that write code.
We built one for what happens after you deploy it.
Can AI resolve production issues? ๐งต [1/N]](https://pbs.twimg.com/media/HTE_FX6WgAAXRqU.jpg)
It's super fun to play with @typesafeai 's #Jev with the #SREGym team. It's crazily fast and unlocks a lot of new capabilities for SRE use cases. A simple integration already leads to promising results, and the potential to scale fast, local reasoning of _all_ system states and to parallelize them is truly exciting.
The team wrote their experience in a blog post (#SREGym has a blog now!), check it out.
Can a small and fast decision model make SRE agents more reliable? ๐ค
We gave an agent access to @typesafeaiโs Jev through the Codex harness and tested it on 10 SREGym-Lite problems.
Three findings stood out. More details in the thread. ๐งต
Seeing the PR to #SREGym by @niallm, an author of the holy Google SRE book (@srebook) and the Reliable Machine Learning book is rather rewarding.
The PR is very SRE style ๐
https://t.co/oLyDk6xcPv
Niall, now a Distinguished Engineer @ciroosai, is also a great, patient mentor for students on the #SREGym Slack.
Glad to see more and more folks like @cerebral_system are using #SREGym to evaluate SRE/Prod agents. It's a great sigh that folks are more serious in building useful agents than claiming fake victories.
Results on low-fidelity benchmarks that do trivial fault injections (e.g., flipping a feature flag in AS) is honestly like the emperor's new clothes. We all know that prod issues are never alike.
Fidelity is arguably the hardest problem in SRE benchmarks. #SREGym doesn't have a perfect solution (there's no free lunch), but pushes very hard on it while balancing cost and reproducibility.
Kudos to @HacksonClark @Yiming_Su3 @SaadMRP @lilygn6 @MunimThahm81566 and many other students who treat eval as a way of understanding the problem, not playing the game.
It's great to see #SREGym is being used for benchmarking SDO!
Vic: Let @HacksonClark @Yiming_Su3 @SaadMRP @lilygn6 know if there's anything you need from #SREGym to support your work.
Early results on microservice benchmarks: architectural context cuts deployment iterations by 2.5x. Seeding the knowledge base with prior post-mortems reduces MTTR by 38% on SREGym.
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