Operations | Monitoring | ITSM | DevOps | Cloud

If you’ve been online in the last fortnight, you’ve probably seen ServiceNow’s “Kevin” memo, the fictional 2028 post-mortem about an enterprise where the AI agents won, the governance team was eliminated, and a single AI governance lead named Kevin spent two years filing risk assessments that were auto-resolved before anyone read them.

SQL Analytics has always been one of my favourite SquaredUp features. That's not just because I can use raw SQL to achieve complex data transformations. The fact that I can run SQL queries over data from all sorts of sources — not just relational databases, gives incredible power and flexibility. The great news is that SQL Analytics now ships with our AI-driven SmartAssist technology.

GPU infrastructure is expensive and increasingly central to production workloads. Whether you’re running ML training jobs, inference serving, video transcoding, or HPC workloads, understanding what your GPUs are actually doing, and what’s going wrong when performance degrades, is not optional.

In the latest episode of This Month in Datadog, Jeremy shares how to run autonomous Cloud SIEM investigations, remediate vulnerabilities with auto-generated fixes, and use natural language to explore Datadog. Later, Sumedha Mehta spotlights the Datadog MCP Server, which gives AI agents real-time access to Datadog’s observability data. Then, Chetan Sharma walks through Datadog Experiments, which measures how product changes impact the user journey.

When I read about the Vercel breach tied to a Context AI compromise, I wasn’t surprised. I’ve been talking with customers for a while now about how AI was going to introduce a new kind of supply chain risk. This is exactly what that looks like. What stands out to me is how familiar the pattern is. We saw it with open source, then again with SaaS, and again with cloud.

AWS re:Invent 2025 made one thing very clear: enterprise interest in AI is no longer theoretical. The conversation has moved beyond curiosity. Teams are actively experimenting, leaders are looking for production-ready use cases, and engineering organizations are trying to figure out where AI can create real leverage across software delivery, security, platform engineering, and operations.