Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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.

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.

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.

In our previous post, we addressed the most common questions platform teams are asking as they prepare for the retirement of the NGINX Ingress Controller. With the March 2026 deadline fast approaching, this guide provides a hands-on, step-by-step walkthrough for migrating to the Kubernetes Gateway API using Calico Ingress Gateway. You will learn how to translate NGINX annotations into HTTPRoute rules, run both models side by side, and safely cut over live traffic.

Enterprise Management Associates found that major IT service outages are increasing in cost, frequency, and duration, with unplanned downtime costing large enterprises nearly $25,000 per minute, or $1.5 million per hour. When every minute costs $25,000, you can’t afford to waste engineering time on coordination tasks like creating channels, paging experts, typing summaries, and posting updates. An agentic AI-powered incident assistant can eliminate that waste and reduce bridge call costs.

You might expect an AI-SRE agent to target 100% reliable services, ones that never fail. It turns out that past a certain point, however, increasing reliability is worse for a service (and its users) rather than better! Extreme reliability comes at a non-linear cost: maximizing stability limits how fast new features can be developed, dramatically increases the operational cost, and reduces the features a team can afford to offer.