AI agents invade observability: snake oil or the future of SRE?
AI agents are emerging in observability and SRE, automating tasks and transforming monitoring. However, skepticism persists due to past AI failures, highlighting the need for benchmarks and addressing privacy concerns.
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The article discusses the emerging trend of AI agents in the observability and Site Reliability Engineering (SRE) sectors, questioning whether these advancements represent genuine innovation or merely another instance of overhyped technology. Historically, observability startups have focused on transforming operational data into actionable insights, often relying on dashboards and alerts. However, with the rise of generative AI, particularly large language models (LLMs), there is potential for a significant shift in how monitoring is conducted. New startups are developing AI agents that can automate various operational tasks, such as incident response and routine maintenance, positioning themselves as integral members of operations teams. Despite the promise of these technologies, skepticism remains due to past disappointments with AI in monitoring, where machine learning solutions failed to deliver substantial improvements. The article highlights the need for benchmarks to evaluate the effectiveness of these AI agents in real-world scenarios, as well as concerns regarding data privacy and the potential costs associated with deploying such technologies. As the industry evolves, the impact of AI agents on observability practices and the future of SRE remains uncertain.
- AI agents are emerging in the observability space, potentially transforming monitoring practices.
- Startups are developing AI agents for automating operational tasks, but skepticism exists due to past AI failures.
- The need for benchmarks to assess AI agent effectiveness is emphasized.
- Data privacy and regulatory concerns are significant challenges for the adoption of AI agents.
- The financial implications of deploying AI agents could be substantial for organizations.
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10 commentsCompare this to codebase AI, where much of the data you need lies in your codebase or repo. Even then, most of these coding tools aren't even close to automating meaningful coding tasks in practice, and while that doesn't mean they can't in the future, it's a long ways off!
Now in the ops world, there's little to no guarantee that you'll have relevant diagnostic data coming out of a system that you need to diagnose it. That weird way you're using kafka right now? The reason for it is told via oral tradition on the team. Runbooks? Oh, those things that we don't bother looking at since they're out of date? ...and so on.
The challenge here is in effective collection of quality data and context, not the AI models, and that's precisely what's so hard about operations engineering in the first place.
IME benchmarks, though valuable, don't fully reflect the real world, often only reflecting the easily quantifiable. The best way is to be able to quickly try out an agent to see how it performs on your work environment. Sort of like having a private test set you can try different agents on to see how they perform in the real world quickly.
Disclaimer: I'm building MinusX, a data science agent (github.com/minusxai/minusx)
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?
As an aside - this is garbage attempt at an article, kinda saying nothing.
I can't solve the article.
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