Operations | Monitoring | ITSM | DevOps | Cloud

Most AI spend comes in with no tags and no owner attached. Your provider console shows total spend, maybe broken out by API key or model. It won’t tell you that the sales team spent $1,700 on Claude this week, let alone what the work was. And the problem is growing. McKinsey found that 56% of organizations now use AI in three or more business functions. More teams means more spend, and most companies respond with a spending cap. Set it too low and you slow down the work you wanted AI to help with.

Ubuntu 26.10 “Stonking Stingray” arrives at a time when AI tools are accelerating vulnerability discovery. Patching faster is essential, but the resilience of the underlying operating system must also limit what an attacker can achieve before a fix is available.

After-hours calls can create missed messages and send urgent issues to the wrong clinician. For private medical practices, that risk grows when call answering and on-call escalation rely on the same loosely defined workflow. Answering service receives an inbound call, captures information, and categorizes the request. On-call routing identifies the responsible clinician, delivers the message, and escalates it when the clinician does not respond.

Why is the telemetry AI agents generate so intimidating? If you turn on Claude Code’s internal telemetry it’ll throw a wall of text at you. And, it’s very expensive to store. But, the bigger issue is that you can’t make sense of it. Luckily it’s all OpenTelemetry native. That means you can configure it to send, transform, and store what you really need. Which raises the only question that matters. What do you actually need?

Western Canada is becoming a live test of whether sovereign AI capacity can be built responsibly at scale, and the answer will depend less on what operators promise than on what they can measure and show. Meta’s planned C$13-billion Alberta data center, BCE’s expansion of its Saskatchewan project to a 1.2-GW hub, and the federal Responsible Data Centre Development Principles all point to the same requirement: operational transparency that regulators, utilities, and communities can verify.

Most IT teams now look after more sites than they have people to visit. Remote infrastructure management covers that gap, and it works differently from the IT infrastructure management you run inside a building where somebody can walk over and look at whatever broke. The technology rarely causes the trouble. Trouble starts when a site goes quiet and there's nobody standing there to look at it.

If your company relies on multiple databases and applications, keeping track of database performance can become difficult. You may face challenges such as: As your database environment grows, identifying the cause of performance issues becomes harder without the right monitoring in place. I get why database monitoring tools have become an important part of managing database performance. They help you monitor database activity, spot performance issues, and find what is causing a slowdown.

How governed AI agents inside a Databricks perimeter can turn customer context into revenue for Communications, Media, Entertainment, and Gaming (CMEG) industries.

The regression nobody catches passes a busy review and ships. An off-by-one, a change that reads as sensible and quietly breaks something, gets a nod from a tired reviewer and lands in production, where it erodes trust one small defect at a time. You can have an AI code reviewer running on your own repository in the time it takes to read this page. Get started without having to book a demo or contact sales.

The "C" word is a controversial subject in the US, but we have to talk about "context", and what it means to an AI Agent. For starters, agents can only act on what's in their context window. Everything outside of it is a guess. In application code that limit is usually an annoyance.