When is it ok or not ok to trust AI SRE with your production reliability?

There’s a moment every engineer knows.

An AI suggests a fix, it looks reasonable,maybe even obvious, but production is on the line and you hesitate before clicking execute.

There’s a big difference between an AI that can recommend an action and one you’re willing to let take that action. All it takes is one bad call, one kubectl command that makes things worse, and suddenly every automated suggestion is a potential liability instead of a help.

Gartner predicts that by 2029, most organizations will experience an AI-related outage. That doesn’t mean teams will abandon AI SRE. We’re actually seeing more adoption of AI because of the increased need for speed and the rising complexity of modern systems. What it does mean is that trust, safety, and knowing when AI should act or hold back matter more than ever.

AI SRE tools are everywhere right now. New ones appear weekly, each promising faster MTTR, better RCA, and fewer late nights. Some are genuinely impressive. But most leave teams asking the same uncomfortable question:

Can I actually trust this thing to act when it matters?

It’s not about surfacing data or summarizing logs. You need an AI SRE that can make the right call under pressure while taking your entire system into account, but know when not to act if the situation is unclear.

The hesitation makes sense. Trusting that your system will stay reliable doesn’t come from data alone. It comes from experience: seeing the same failure patterns repeat, knowing which signals matter and which ones don’t, understanding how small changes cascade through a cloud-native platform. It’s knowing which fixes are safe, which are risky, and which ones shouldn’t be automated.

Many AI tools are good at analyzing signals. Very few understand failure. Even fewer understand your failure — the tribal knowledge that lives in postmortems, Slack threads, and inside the heads of senior SREs. Without that context, AI is just super-fast guesswork. And guesswork doesn’t earn trust.

Of course, a self-healing autonomous system doesn’t have to be all-or-nothing. It can be anything from basic assistance to carefully bounded execution, to confident autonomy. The mistake many AI SRE tools make is jumping straight to action without proving they’ve got an accurate and reliable playbook or waiting for a human in the loop. 

Klaudia, the agentic AI that powers the Komodor AI SRE Platform, is designed to work like an experienced SRE teammate, not a replacement.  It can start with more low-level straightforward use cases that senior engineers are comfortable signing off on. For example, this might include low risk actions like Helm rollback, pod restart, or memory increase. Later, you may feel comfortable letting Klaudia automatically make adjustments that prevent issues from escalating into incidents.  Guardrails define where it can act, and those boundaries can be expanded naturally as the system proves itself. Over time, Klaudia doesn’t just suggest what to do; it demonstrates that it knows when and why.

The point of AI SRE is to reduce toil, cut down MTTR, and prevent the same issues from resurfacing by improving system reliability.  When each recommendation is tracked and measured for success, the system continues to learn from every incident. Over time, this helps improve reliability and accuracy, laying the groundwork for autonomous remediation you can trust. It’s also key that the system be proactive, not just reactive, whether it’s identifying real cost-savings opportunities, or recognizing early warning signs and triggering preventative measures.

None of this matters, though, unless the system is consistently accurate.

This blog series explores what it actually takes for an AI SRE to earn trust, and how experience contributes to investigation and confident action. We’ll look at where real AI SRE value shows up first, how Komodor’s AI SRE operates during a live incident, and why an experience-driven, agentic approach leads to systems that can be fixed faster and are genuinely more reliable.

The goal isn’t to take the driver out of the seat. It’s to make the drive safer, smoother, and less exhausting, so even when the road gets difficult, engineers can focus on getting where they need to go.