Operations | Monitoring | ITSM | DevOps | Cloud

Modern IT environments are noisy. The sheer volume of telemetry data coming forth every second from microservices, hybrid clouds, and containerized applications is just extraordinary. In IT Operations, NOC teams, and Site Reliability Engineers (SREs), this data is crucial, but only if it can be acted upon. When it’s not like this, everything becomes a background noise.

In late 2025, we introduced Trace Highlight Comparison. This capability was designed to solve the problem of having too many spans. This causes technical and financial challenges when identifying performance patterns within high-volume telemetry streams. The goal is to avoid massive indexing costs and eliminate the ingestion latency associated with indexing every record. However, knowing these trends is only half the battle.

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.

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.