Why AI Isn’t the Magic Wand with Observability
Hey there 👋🏼,
AI is everywhere — from AI-augmented DevOps pipelines to autonomous infrastructure remediation, the cloud isn’t just getting faster; it’s getting smarter.
Naturally, observability is getting pulled into the AI conversation. At first glance, the idea is exciting: throw your logs, traces, metrics, and changes into a large language model, and let it explain your system like a seasoned SRE. In practice, though, it’s more complicated. AI thrives on structure and clarity, but modern systems rarely play by those rules.
Ogranisation like MCSI use tools like trace explainer, log explainer, and metric explainer to provide automated insights into system behavior by analyzing traces, logs, and metrics. The problem arises at scale. In large systems, a single API request might generate thousands of spans (small events within a trace). If you try to send all of that raw data into a large language model (LLM) like GPT, you're likely to hit two issues: inconsistent and misleading outputs.
In reality, the future of observability isn’t about handing over control to AI. It’s a collaborative space where human expertise and machine intelligence work in tandem to decode the complexity of modern systems.
✨ FEATURED ARTICLE
AI is poised to reshape many industries, but one fundamental truth remains: AI and engineering must complement each other. LLMs alone cannot solve all problems, but when paired with strong engineering fundamentals... This article answers the question: Can Observability Keep Up With LLMs?
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