What is an AI SRE?
May 12, 2025
Zuri Obozuwa
Founder @ Icosic AI
An AI Site Reliability Engineer (AI SRE) is an autonomous AI agent that diagnoses system incidents and outages. This article explains the role of an AI SRE, highlights its advantages, and outlines how it can significantly improve your organization's reliability and cut down your Mean Time To Repair (MTTR).
Understanding the AI SRE
A human SRE is the engineer whose job it is to diagnose system outages. They are often ‘on-call’, which means that they are responsible for immediately responding to outages, even if that means waking up in the middle of the night.
To investigate an outage, the human SRE often looks through a runbook to determine the best course of action, then looks through observability data such as metrics, traces and logs to attempt to diagnose the root cause of the outage.
An AI SRE uses large language models (LLMs) to investigate system outages by looking through company knowledge bases, metrics, traces, source code and server-side logs, often using runbooks to aid it’s investigation.
Unlike manual, human-only investigations that often take hours, an industry-grade AI SRE finds the root cause of the outage in seconds or minutes.
For example, imagine an e-commerce company experiencing sudden downtime during peak hours.
As soon as an alert is triggered in the company’s observability platform, the AI SRE looks through company knowledge bases, metrics, traces, logs and source code, quickly identifies the root cause as a faulty database query, and provides precise instructions to rectify the outage within minutes - significantly reducing downtime and customer impact compared to slower human-led investigations.
Advantages of using an AI SRE
Greatly reduced Mean Time To Repair (MTTR):
AI SREs can look through company data and observability data to identify the root cause of an outage in seconds, while it often takes a human SRE hours and sometimes days to find the root cause.
For the human SRE, some outages are so hard to find the root cause for that it requires assistance from the entire engineering team for hours or days, sometimes including the CTO and/or the VP of Engineering. Industry-grade AI SREs can often find the root cause of these difficult outages in seconds, especially when equipped with company knowledge.
Reduced Operational Costs:
The average salary of an SRE is around $140,000 worldwide and around $200,000 in California. Automating the root cause analysis stage of incident response means that you often can replace at least one human SRE with an AI SRE, saving you substantial amounts of capital every year.
This is great because for a lot of companies, employee salaries are the largest cost of doing business.
Enhanced Customer Trust
When you have downtime that exceeds your Service Level Objectives (SLOs), it often means your company has to pay hefty fines in the form of Service Level Agreement (SLA) breaches. If that wasn’t bad enough, the affected customers can often choose to switch to one of your competitors if they are known for having higher uptime.
Trust is very hard to regain once it is destroyed. Using an AI SRE signals commitment to maintaining the promised availability and uptime, and it makes it much more likely that you will fulfill those promises.
Higher Employee Productivity
Tough and demanding on-call schedules often make the human SREs tired during the day, which hampers their productivity.
Using an AI SRE will let your remaining human SREs have more sleep, be on-call less often and therefore be more energized and productive during standard working hours
Less Employee Churn
Human SREs often quit their job due to the demanding nature of being on-call. They even express disdain at the perceived ingratitude of their company: they often don’t get paid extra for being on-call
Using an AI SRE greatly reduces the burden of incident response on your human SREs and other related engineers, meaning less employee turnover and therefore less recruitment costs.
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