Operations | Monitoring | ITSM | DevOps | Cloud

Achieving end-to-end observability for applications is a top priority for organizations today, but instrumenting for both frontend and backend monitoring can be a significant hurdle. What complicates matters is that the SREs and DevOps teams responsible for deploying monitoring tools typically don’t own frontend code or have the context needed to safely modify it.

To succeed in the AI era, customers need fast, easy access to security solutions that can harness the power of agentic AI and deliver business outcomes. They need seamless access to their data for faster threat detection, simpler incident response, and reduced risk. They need technology vendors to work together and not in silos.

For a CTO, "four nines" represents a commitment to keeping production revenue live with less than 0.01% of total downtime per year. As AI workloads move from pilot projects into core production services, the reliability requirements for infrastructure have shifted. AI agents, RAG pipelines, and automated LLM workflows depend on a consistent platform state.

During high-priority incidents, SRE teams frequently lose critical time switching between monitoring platforms and ticketing systems. Context switching like this forces engineers to manually update incident states by copying and pasting data. The inevitable result is increased risk of information gaps and slower Mean Time to Recovery (MTTR).

You’ve almost certainly seen them… In the forest, rummaging through a dumpster, in poorly aging millennial memes. Raccoons are ubiquitous and endlessly entertaining creatures. YouTube and TikTok are full of videos documenting their clever antics and escapades. One such intrepid raccoon gained fame for making their way to the most unlikely places, from liquor stores to karate studios.

I was looking at a customer’s error report last week. A TypeError buried three callbacks deep in a checkout flow that made no sense. The code around it was clean, well-structured, and completely wrong about how the Stripe API actually works. Turns out it was vibe-coded. Someone prompted their way through the integration, it passed code review because it looked reasonable, and it worked fine right up until a customer’s card got declined for the first time. That’s the new normal.

Teams put a lot of thought into how critical incidents are handled. Low-priority incidents usually don’t get the same attention. And without a proper escalation policy, they just land in a shared channel, waiting for someone to acknowledge. Setting up a clear policy for them is worth doing. Not because they need the same urgency as a critical incident, but because having a defined path for every incident makes the whole system more reliable.

I've used the Web API plugin with a wide range of APIs, and each one taught me something new. But before diving into building, I learned to pause and ask: What am I actually trying to display? Not what data the API can give me, but what would be useful on a dashboard? That shift in thinking — from ‘fetch everything’ to ‘fetch what matters’ — shapes how I approach every integration.

Jira is the backbone of project management for thousands of agile teams worldwide. But while Jira excels at tracking issues and sprints, its native reporting can leave teams wanting more — especially when it comes to sharing insights, visualizing trends, and integrating data from across the business. That’s where dedicated Jira reporting tools come in. In this guide, we rank the 5 best Jira reporting tools on the market today.

Battery Energy Storage Systems (BESS) represent one of the most unforgiving environments for real-time data. Unlike a passive asset, a battery is a complex electrochemical system where safety and revenue are determined by split-second decisions. In this context, “average” latency can become a serious problem. Performance depends entirely on one key question.