Operations | Monitoring | ITSM | DevOps | Cloud

Before I ever ran Claude in my terminal, I thought I already understood AI tools pretty well. Like most people, I had used ChatGPT, Google Gemini, and Perplexity for everyday tasks. Such as helping with schoolwork, organizing ideas, summarizing information, or getting through something faster when time was tight. They were useful, but they still felt separate from how real work happened.

So your database is slow. Now what? Grafana Cloud Database Observability already gives you visibility into your SQL queries with RED metrics, individual execution samples, wait event breakdowns, table schemas, and visual explain plans. But visibility is just the starting point. You can see that a query's P99 latency spiked, but what should you do about it? You can see wait events like wait/synch/mutex/innodb firing, but what does that actually mean?

AI is moving fast. Enterprise adoption needs to move with purpose. Over the past year, one thing has become clear: Organizations are not looking for more AI hype. They are looking for a path to production — one that connects infrastructure, data, and intelligence in a way that delivers real business value. That is exactly what the Elastic AI Ecosystem is built to do. At Elastic, we believe AI is only as powerful as the data foundation behind it. Great models matter.

meshIQ v12.1 transforms middleware management with petabyte-scale data processing and agentic AI. The new intelligent launchpad, simplified onboarding, and context-aware safeguards move teams from reactive monitoring to proactive, AI-driven operations across the enterprise.

Over the past decade, cloud computing has evolved from an emerging technology into the foundation of modern digital infrastructure. However, the latest industry research shows that the industry has now crossed a critical threshold. The conversation is no longer about whether to adopt cloud, cloud-native technologies, or AI. Instead, it has shifted toward operational efficiency, economic predictability, and infrastructure at scale.

There’s a new question teams are asking. How can we prevent AI agents from deleting production. When Cursor deleted PocketOS’s entire production database in nine seconds, the agent wasn’t malfunctioning. It had full technical capability, but it was inferring operational authority from static code rather than live environment state. That gap between capability and context is the root cause. This article breaks down exactly how that happens, and what runtime visibility does to stop it.