Operations | Monitoring | ITSM | DevOps | Cloud

How do you use ArgoCD and a GitHub workflow together with an external CI/CD tool (Jenkins in our case) to trace a specific PR/commit hash code in a GitHub repository that’s external to ArgoCD? The term ‘commit tracing’ comes from Michael Crenshaw – one of the lead developers of Argo.

Every engineering team eventually faces this question: “Is our CI/CD setup actually helping us, or is it getting in the way?” The answer isn’t always obvious. CI/CD problems often develop gradually: small issues become accepted workarounds, and those workarounds become standard practice. What once worked well for your team might not fit your current needs or scale. The decision to evaluate new tooling usually builds over time as pain points accumulate and priorities shift.

The more organizations depend on collaboration solutions like Microsoft Teams for productivity, the more IT departments are expected to ensure a seamless experience every time. That demands more than just rapid troubleshooting when issues occur: it requires IT teams to get ahead of problems and keep them from affecting users in the first place. For that, synthetic testing is a must.

Few things hold IT teams back more than a lack of visibility. It’s exponentially harder to solve issues when they originate in parts of the environment you can’t see. That’s one of the big limitations of native tools for monitoring and managing Microsoft Teams. Microsoft Call Quality Dashboard, Admin Center and Service Dashboard, and Meeting Room Pro Dashboard are all constrained to the aspects of Teams that Microsoft controls directly.

We’ve just wrapped up London’s 2025 Open Source Finance Forum (OSFF) in London and in this blog I’ll try to capture the key highlights from this year’s event while they’re still fresh. Dominant themes were the increasing prominence of legislation and governance frameworks, and what these mean for developers and practitioners.

An operations team at one of the Asia-Pacific’s largest managed service providers (MSPs) was drowning in their own success. Years of investment in monitoring tools and automation had created comprehensive visibility—and comprehensive chaos. Engineers opened dashboards each morning to find thousands of alerts waiting, with critical incidents buried somewhere inside. The scale of the problem was overwhelming their capacity to respond effectively.

The complexity of modern enterprises has pushed IT operations to the limit. Hybrid cloud environments, CI/CD pipelines, microservices, and agile methodologies revolutionized IT, but caused an explosion of scale and data fragmentation. This complexity simply cannot be managed by legacy tools or manual ITSM processes designed for monolithic systems and static infrastructures.

You can write clean code, test obsessively, and deploy with crossed fingers…but errors always find a way. And if you’re not tracking them? You’re gonna get bugged. Whether it’s a silent failure or a full-blown crash, every unreported issue is a hit to your app’s reputation.

If your legal department is anything like ours, you’re being inundated with requests from the business to use more and more AI tools. Whether it's developers wanting to use coding agents like Cursor, to security implementing AI-driven investigations, to sales and marketing leveraging AI for call insights and competitive research, we've seen a shift in what teams are trying and buying.

You deploy a fix late Friday and spend the weekend refreshing dashboards, hoping nothing breaks. You shouldn’t have to babysit a dashboard to know when something’s wrong. With the latest updates to Insights, you can now create alerts directly from any chart. Whether it’s a spike in 4xx errors after a deploy, a jump in P95 latency for an API endpoint, or a drop in throughput for a background job, you can set up alerts with just two clicks.