DevOps / SRE / Platform · 30.07.2026, 20:33 UTC
Why your company should (try to) build its own AI SRE
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 30.07.2026 UTC |
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Companies ship more code today than ever, thanks to agentic coding tools. For product teams, this accelerated pace of development is a boon. But for site reliability engineers (SREs), the influx of new, machine-written code is not.
Why? Because when something breaks, no human fully understands how the underlying system works. Sorting out what went wrong, how to fix it, and how to avoid the problem in the future can be both difficult and time-consuming. All while time constraints remain static, as downtime is no more welcome today than in the past.
You can easily burn out your humans by tasking them with sorting out how to fix what agents built; that’s backward.
If agents are writing code, and humans are having a hard time keeping up, is the solution to fight fire with fire — to deploy AI agents to sniff out root problems in misbehaving systems? Yes. The good news is that as AI models have improved, the capability to execute root-cause analysis is improving, though the OpenRCA benchmark remains far from saturated.
Thanks to those performance gains, Sam Farid and Nate Heinrich of Chronosphere say in this latest episode of The New Stack podcast that agents are the way forward. And the pair think that your company should try to build an AI SRE in-house — before exploring vendor offerings.
Chronosphere offers an AI SRE product, so why are its forward-deployed podcasters arguing for homegrown alternatives? Heinrich told me that he tries to convince people to build their own agents because doing so is a useful way to collect and organize information about how a company’s …