Operations | Monitoring | ITSM | DevOps | Cloud

London has spent decades establishing itself as one of the world’s major technology hubs. Its strength comes from the concentration of AI and technology startups, financial services and fintech, highly regulated industries, universities and research institutions, international businesses and technology companies, alongside a large community of developers and engineers.

Obkio is making improvements to its network performance monitoring and observability solution, aimed at helping users of every expertise level interpret their data more efficiently. That work isn't just about telling users something is wrong. It's about diagnosing the issue for them. And a big part of that is telling them where the issue is happening, so they know exactly where to direct their troubleshooting effort instead of guessing. The Status Overview widget solves this.

We hosted a live launch preview, “Level Up Your Agentic Workflow,” to introduce two things at once: GitKraken Insights for Developers and Kepler. Jeremy Castile, our VP of Developer Research, opened with research on how teams are actually using AI agents today. If you want the full breakdown of that data, including the adoption numbers and the gap between feeling faster and proving it, we published it separately in State of AI in Engineering 2026: The Proof Gap.

Most CSP assurance roadmaps now carry an AI line item. Fewer have a clear answer for what that AI actually runs on. Over the past year, the working question across operators and vendors has narrowed to something practical: how to put agents to work in assurance while keeping operators in control.

Most MSPs are already experimenting with AI in some form, and every vendor at every conference has an AI for MSPs pitch ready. Few have stopped to check whether their own operations are solid enough to scale. Our 2026 IT Trends Report found that three-quarters of IT leaders believe they have an AI policy, while fewer than half of help desk staff agree. That’s the real risk: AI doesn’t fix drift, unclear ownership, or gaps between what’s documented and what’s actually happening.

Microsoft’s Azure Functions serverless compute service abstracts away infrastructure management to simplify how teams deploy and operate applications. However, the managed environment can make it harder to understand what happens inside those applications after deployment. Traditional approaches that rely on agents, extensions, and sidecars for direct collection of telemetry data aren’t available across every Azure Functions hosting plan, which can leave gaps in end-to-end visibility.

Resilience Testing (RT) Agents scan your CD pipelines and Kubernetes workloads for resilience risk before it reaches production, then generate and run the chaos experiments or load tests needed to confirm it.

IT Ops teams face two failures that pull in opposite directions: responders interrupted so often that they stop reacting, and a truly critical alert lost inside that same flood. Alert noise and alert fatigue describe those two problems, and treating them as one usually makes both worse. This article shows how to reduce interruptions without making critical incidents easier to miss.

The AI red teaming market grew up fast this year. OpenAI bought Promptfoo, Cisco and Microsoft shipped automated attack suites, and a seed-stage startup publicly compromised 50 of 55 live customer service bots. These platforms find real problems at a scale no human team can match. But when you read the findings closely, a pattern emerges: agents talked into refunds, transfers, and data leaks they had standing authority to perform. Patching the prompt fixes one phrasing until the next model update.