Operations | Monitoring | ITSM | DevOps | Cloud

When an incident lands in your lap, you’ll often start with a lot of questions: Why is latency so high? What’s causing this outage? How much money are we losing at this very moment? The uncertainty—and the pressure to quickly find answers—has always been one of the more nerve wracking parts of being an on-call engineer, but it doesn’t have to be that way any more.

I have begun to truly embrace vibe coding over the last few months, using Cursor as my main code editor and Claude Sonnet 4 for my agent's LLM. It's an exciting time as a developer, we get to experiment with something that promises to 100x our productivity while pioneering the new workflows and strategies for implementing these tools. But, as most people who have done any extensive development with LLMs in a sufficiently sized code base knows, it's a bit like trying to herd scared cats.

Organizations are adding GenAI to their current and future architectures and product roadmaps, requiring Ops teams to ensure LLMs are accurate, fast, secure and cost-efficient. LLM observability tools directly addresses these needs, helping identify and prevent common LLM errors and issues: LLM observability provides the telemetry data for this analysis. LLM observability tools trace requests end-to-end, evaluate outputs, and correlate quality with latency, cost, prompts, tools, and data sources.

Everyone is excited about AI, but few companies have successfully implemented it. While enthusiasm for generative AI (GenAI) has helped accelerate AI adoption across enterprises, the promises of artificial intelligence have yet to translate into measurable impact on most organizations’ bottom lines. The trouble isn’t the tech — it’s a lack of executive ownership.

If you could run public and private cloud like they were the same place, what would that unlock for your teams? That was the core message of our Navigate London keynote, and it matters just as much to readers in Singapore or Seattle as it does to those who joined us in person. For years, the industry has made you choose. Public cloud on one track. Private cloud on another. Different APIs, different skills, different bills, and a rewrite every time you move.

Generative AI can produce code faster than humans, and developers feel more productive with it integrated into their IDEs. That productivity is only real if CI/CD tests are solid and automated. When not appropriately tested, you may encounter a production issue that you haven’t seen before. According to the State of Software Delivery 2025 report, 67% of developers spend more time debugging and resolving security vulnerabilities in code generated by AI.