Comment by zug_zug

Because the people creating and selling these solutions are charlatans who have probably never debugged a gnarly Linux issue; more importantly, they’re selling it to people who also don’t know any better.

SRE as a term is effectively meaningless at this point. I can count on one hand the number of SRE coworkers I’ve had who were worth a damn. Most only know how to make Grafana dashboards and glue TF modules together.

Exactly, because the major thing every company needs is an opaque agent messing with all the production settings and releases that impact revenue and uptime? This is one of the silliest pitches I've heard in my life.

I'm all for a k8s / Terraform / etc focused GPT trained on my workspace to help me remember that one weird custom Terraform module we wrote three years ago, but I don't want it implementing system wide changes without feedback.

Noticing trends in logs ,especially across different systems, could be useful for identifying a lot problems before they break.

>I mean is the AI going to read your sourcecode, read all your slack messages for context, login to all your observability tools, run repeated queries, come up with a hypothesis, test it against prod? Then run a blameles retrospective, institute new logging, modify the relevant processes with PRs, and create new alerts to proactively catch the problem?

Yes. That's exactly where this stuff is going right now.

Agents do not necessarily need to do the entire job of an SRE. They can deliver tremendous value by doing portions of the job. i.e. bringing in relevant data for a given alert or writing post-mortems. There are aspects of the role that are ripe for LLM-powered tools

> As an aside - this is garbage attempt at an article, kinda saying nothing.

Disagree. It was a good survey of the current AI SRE agents landscape. I don't have my head in the sand, but there are new startups coming up that I hadn't heard about.

>I mean is the AI going to...

Yes. Not one AI but multiple AI agents, all working on different aspects of the problem autonomously and then coming together where needed to collaborate. Single LLMs doing things by themselves are like concrete, creating swarms of agents is like adding rebar to the mix.

As with Asimov's The Last Question, there will be insufficient data for a meaningful course of action. But we tend to cut corners, be convinced with some synthetic use cases and take the risk without being aware that they are so. The mistakes will be dismissed as attributed to misuses, or weird installations, and the successes will fuel the hype.

They're going to need SRE for the AI :)

I mean, doesn’t all that sound like something fine tuned LLMS would be great at? Well constrained tasks, if else statements, runbook -> automation.