Operations | Monitoring | ITSM | DevOps | Cloud

CI/CD pipelines do four things: it builds code, runs tests against mocked dependencies, lints for style violations, and scans for known vulnerability patterns. What it cannot do is validate how that code behaves under real users, real service responses, and real runtime constraints that staging was never configured to reproduce. That entire class of failure clears every gate cleanly and surfaces only in production.

If you've read our previous piece on the 8 stages of AI engineering maturity, you know where your team sits. Turns out adopting AI is the easy part; adapting to its consequences is where most organizations struggle. For more than a decade, software organizations optimized around a single assumption: implementation capacity was scarce.

IT infrastructure automation sounds simple enough on the surface, right? You take repetitive infrastructure work, turn it into automated workflows, and give engineers more time for higher-value problems. This may seem easy, but in practice, it gets more interesting. Modern IT environments are spread across cloud platforms, legacy systems, identity tools, ITSM platforms, monitoring systems, network devices, and business-critical applications.

AI pricing covers the cost structures and billing models providers use to charge for AI products: per-token APIs (GPT-4o at $2.50/1M input tokens), per-seat subscriptions (Copilot at $30/user/month), per-conversation billing (Agentforce at $2/conversation), and consumption-based GPU compute (H100 instances at $55.04/hour). There is no standard. The total AI cost is almost always higher than the sticker price.

In Explorer, you build a filter set and group-by to answer a cost question, and often that’s exactly the configuration you’d want to save for later. But saving it as a View meant navigating away from Explorer, opening the Views page, and rebuilding the same configuration from scratch: filter by filter, dimension by dimension. That friction was enough to discourage saving exploratory analysis as a View at all You can now save any Explorer analysis as a View in place.

Canonical’s security philosophy has always been built on the premise that vulnerabilities exist and will be discovered. Our response relies on defense-in-depth architecture, rapid patch deployment, and strict adherence to Coordinated Vulnerability Disclosure (CVD). AI changes vulnerability discovery volume and speed. We have a robust vulnerability management process that is backed by rigorous compliance certifications.

As designers working at Canonical, we’re always thinking about open source. We believe that encouraging more designers to contribute to open source benefits everyone, from the project maintainers to the end users themselves. In the 2025 edition of FOSSBackstage conference, we presented our research findings on why designers don’t get involved in open source projects and found a particular breakdown between designers and project maintainers.

Engineering leadership is in the middle of a real transition, and most of the leaders I talk to know it. AI has reshaped how software gets built quickly enough that the operating models many of us spent a decade refining no longer fit cleanly, and there is a great deal of serious work happening across the industry to figure out how these models should evolve. The teams I find most impressive right now are the ones treating their operating model as an open question rather than a settled one.

Today I'm sharing some big news. IsDown is joining UptimeRobot When I started IsDown, the idea was simple. Keeping track of outages across dozens of vendor status pages was painful, and I wanted to make it easy to see, in one place, when the services you depend on go down. Thousands of teams now rely on IsDown to do exactly that. Joining UptimeRobot is the natural next step.

Over the past decade, enterprises have invested heavily in observability platforms designed to deliver comprehensive insight into increasingly complex environments. Modern systems generate continuous telemetry across infrastructure, applications, networks, cloud services, and third-party dependencies. Metrics, logs, traces, and topology maps now provide a level of technical transparency that would have been difficult to imagine only a few years ago.