What is an AI SRE?

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Production's down, your pager's going off, you're scrolling through Datadog, you're checking logs, you're forming hypotheses. Does this sound familiar? Well, now imagine a team of AI agents doing all of that investigative work for you.

In minutes.

Not hours.

That's what we're building at Resolve AI. I'm Justin Smith, and today I want to talk to you about what an AI SRE actually is.

Beyond ChatGPT: What Makes a True AI SRE

Most people think that an AI SRE is just slapping ChatGPT on your incident response workflow, but that's actually not gonna cut it. A true AI SRE is your agentic interface to production. It understands your infrastructure, it understands your code base, your change events. It integrates with all of your observability tools, Grafana, Dynatrace, whatever it is that you're running.

But here's the part that everybody actually misses. It has to understand your institutional knowledge as well. The runbooks, but also all that tribal knowledge that lives inside of your head as a senior engineer, that stuff that you can't, or that you just don't document down. When you put all of that together, you actually get a multi-agent system that can reason across your production environment.

It can use tools like an engineer would, it can actually deliver an impactful root cause analysis - not just a surface level alert, initial triage, but a true RCA. And that's what an AI SRE should be. Without autonomy across all of those dimensions, you haven't really yet solved the core problem.

Why Out-of-the-Box LLMs Fall Short

So, why can't you just use Claude or GPT-4 for all of this? Well, here's my litmus test. How much do you actually trust it to run in production? Not how well it demos or performs in a one-off kind of instance, but how much do you actually trust it to do all the day-to-day stuff? Using an out-of-the-box LLM for production -- it's like hiring a really brilliant generalist who's just never seen your actual stack. Sure, they're really, really smart, but they don't know your environment.

And so, there's three big problems:

The first one: complexity. Production - it's not just your app, it's everything from the application code down to the infrastructure, dependencies, everywhere. LLMs with tool access can help, but they max out pretty quickly.

The second is specialized tools. You can't just hand kubectl or Datadog to an LLM and expect magic. These tools require real expertise to use them effectively.

And the third is knowledge. The stuff that makes you good at running production systems. Half of it is documented and the other half lives in some slack thread from a few months ago, or the brains of the people that have been here for years and years and years. And you can't capture that with traditional scripts and automation. It's too rigid. So you need a system that can adaptively learn, and this is generally a very, very hard problem. It requires deep AI experience and deep domain experience and what it means to truly be an SRE.

The Multi-Agentic Solution

So if you can't just use an out-of-the-box LLM, what's the answer? Well, you need an AI SRE that is composed of many AI agents. Now, when I say AI agents, I'm talking about a team of them all working together in what we call a multi-agentic system. It's a system that you can trust to operate autonomously to solve complex problems. So what does it mean in practice for an AI SRE?

Well, there's four things:

The first one is goal orientation and planning. It knows what it's trying to achieve and how to get there.

The second one is environmental perception. It can interact with your tool chain. It can read metrics, it can query logs, it can check for deployment history.

The third one is reasoning and hypothesis generation, and this is the big one, when an alert fires, it doesn't just report data, it begins to form hypotheses. There's a latency spike, hypothesis one, there's a recent deployment, let me check the canary metrics. Okay, all that kind of comes back clean. Hypothesis two, maybe there's a database connection, pool exhaustion. Let me go query the database metrics. It's this iterative, hypothesis driven reasoning that actually gets you to the root cause, not just throwing data back at an engineer for them to go figure it back out.

The fourth is just memory and learning. It remembers past incidents and what worked. If a specific sequence of steps found a memory leak last time, it'll prioritize that workflow for the next time that this similar alert fires. So short-term memory for the current incident, long-term memory to get better over time.

Transforming Your Workflow

So how does adopting an AI SRE with all of these agentic capabilities transform your workflow?

Well, traditionally, you'd onboard a new engineer for six months or so, they'd learn all the runbooks, absorb the tribal knowledge. They'd shadow all the senior engineers, and then maybe they'll be able to go on call. But with an AI SRE, like Resolve, root cause analysis happens in minutes. It's not days, it's not hours, it's minutes.

And that doesn't just make your team faster. It fundamentally changes how you begin to think about reliability and how you manage being on call.

Why Adopt an AI SRE Now?

So who should be adopting an AI SRE and why right now?

Look, there's a lot of AI FOMO out there. Everyone wants to stay current. But here's the real reason. We're generating way more code than ever before with AI assistance. Claude Code, Cursor, all these tools are amazing, but that code, it still has to run reliably in production. The velocity is increasing, the complexity is increasing, and traditional approaches to reliability are just not scaling with all of this.

And that's the gap we're filling at Resolve AI. So the next time your pager goes off at 3:00 AM, you'll have an AI SRE that's already starting to investigate.

Ready to Try an AI SRE for Yourself?

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