Operations | Monitoring | ITSM | DevOps | Cloud

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.

The obituary for the data center was written too soon. While the cloud dominates today’s IT headlines, traditional infrastructure hasn’t disappeared. It is evolving. Enterprises still rely on data centers for control, compliance, and reliability. However, they are increasingly needing the agility, scalability, and cost visibility that the cloud promises. Cloud managed data center services are bridging this gap.

You can’t blame them. Despite all their shortcomings, legacy systems are so tightly woven into vital operations across industries that eliminating them is far from straightforward. The real problem is not the systems themselves, but their low sense of adaptive capacity. Legacy architectures can’t anticipate what the next disruption will look like.

As some of you might have already noticed, we recently gave our official Icinga 2 container image builds a complete overhaul. These new images are currently available only as snapshot builds but will replace the existing stable images with the next Icinga 2 v2.16.0 release. In this blog post, we’ll walk you through the key changes and improvements that come with the new images, as well as the reasons behind these changes.

Not long ago, digital employee experience (DEX) was just a line on an IT report — something to track uptime, device issues and help desk tickets. Those metrics matter to IT, but they don’t always resonate with the C-suite. But behind the numbers is a larger story: every slowdown, every frustrating login, every delayed ticket chips away at productivity, engagement and business results.

I’ve been in those late-night "war room" calls where, after hours of painstaking work, the team finally resolves a critical outage. The dashboards all turn green, a collective sigh of relief is shared, and the next day’s report highlights a victory: Mean time to resolution (MTTR) was reduced by 15% compared to the last major incident. It feels like a win.

Today’s enterprise networks are a far cry from the centralized, predictable infrastructures of the past. Instead, they are sprawling, dynamic ecosystems that stretch across cloud services, SaaS applications, on-premises data centers, distributed branches, and thousands of end users connecting from every imaginable location. This complexity creates a huge challenge for IT and network operations teams: How do you get a clear, real-time view of what’s really happening?

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.

Here's some excellent news for the Pythonistas in the room: You can now monitor the performance of your Python applications with Honeybadger. Last year, we launched Honeybadger Insights, a new logging and observability tool bundled with Honeybadger. Insights enables you to query your application logs and events to answer performance questions, perform root-cause analyses, and create charts and dashboards to see what's happening in real time.