Operations | Monitoring | ITSM | DevOps | Cloud

It has become common knowledge that the nature of software development has changed as AI-code generation and agent-based features gain adoption. In perhaps a more subtle shift, the fundamentals of software instrumentation are changing too. As OpenTelemetry becomes the standard instrumentation layer across enterprises, with thousands of developers (many from Honeycomb) actively contributing to it, the nature of the telemetry data captured itself is evolving to meet the growing demand for rich context.

The InvGate Asset Management Agent is a lightweight piece of software that runs on your endpoints to collect essential hardware and software data. Organizations use it to gain full visibility into their IT environment, keep inventories accurate, understand real software usage, and support remote operations, all as part of a broader IT Asset Management strategy focused on control, optimization, and compliance.

You’re reading an old performance article, and it keeps talking about “First Meaningful Paint.” You search for how to improve it, but every tool gives you different advice. Some don’t mention it at all. What’s going on? Here’s the short answer: First Meaningful Paint is dead. Google deprecated it in Lighthouse 6.0 back in 2020 and removed it completely in Lighthouse 13. If you’re still trying to optimize for FMP, you’re chasing a ghost.

AI code generation is fast. Making it reliable requires runtime context. Today, Kiro gains live runtime visibility with the Lightrun MCP. This grounds AI-assisted development in how code actually behaves at runtime. Kiro, the AI coding assistant from the teams at AWS, is built for velocity and intuition. It moves from specification to production with speed and structure, helping teams turn intent into working code. But until now, like every AI coding assistant, Kiro had a major blind spot.

It’s your site’s huge, annual sale weekend, and your online store’s checkout process went down for 10 minutes. At your conversion rate, that’s $10,000 in lost sales. Thankfully, it came back up after only 10 minutes, but the real issue is that you only found out from customer complaints on social media. You spent months on email marketing and other campaigns driving traffic to this sale, and now those efforts are turning into customer frustration instead of revenue.

In 2026, we are seeing incredible feats of engineering with agentic AI, impacting metrics and distributed traces that map thousands of microservices. Our systems have never been more intelligent and complex. However, as our observability becomes more intelligent, fewer employees know how to manage and troubleshoot complex systems. These employees, who often bear the brunt of an error’s impact, may need to rely on specialists to interpret the system.

Creating a custom dashboard is the best way to monitor metrics that matter most to your systems. Tools like MetricFire make this process straightforward by combining hosted Grafana and Graphite, eliminating the need for self-hosted solutions. Here's how you can build dashboards tailored to your needs.

Setting up alert thresholds in Graphite transforms raw monitoring data into actionable notifications, helping you address system issues before they escalate. Here's what you need to know.

Despite the enterprise-wide pivot toward digital deflection, voice remains the primary escalation channel for high-complexity customer issues. Yet, while organizations rigorously optimize digital touchpoints, telephony frequently remains a siloed legacy endpoint, disconnected from the broader CRM architecture. This integration gap creates a strategic blind spot that fundamentally undermines your digital roadmap.

History is everywhere at Dartmouth College, and while the campus is steeped in tradition, its IT infrastructure can’t afford to get stuck in the past. In an institution where world-class research and undergraduate studies intersect, technology must be fast, invisible, and – above all – reliable. That reliability was put to the test when Dartmouth’s load balancing vendor was acquired twice in five years, as Avi Networks moved to VMware and VMware moved to Broadcom.