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The latest News and Information on Monitoring for Websites, Applications, APIs, Infrastructure, and other technologies.

AI observability: Why traditional monitoring isn't enough for AI systems

Traditional monitoring watches four things: whether the system is up (availability), how fast it responds (latency), how often it errors (error rate), and how hard the underlying resources are working (resource utilization). This system works because software is deterministic: the same input produces the same output, so a deviation means something broke, and it announces itself as a timeout.

Top tips: What happens when boredom disappears?

Top Tips is a weekly column where we highlight what's trending in the tech world and share practical ways to stay ahead. This week, let's look at what happens when we fill every spare moment with digital content, why a little boredom might be useful, and how to make room for it in our screen-filled lives. Imagine you're waiting for your coffee. It's going to take three minutes, so you reach for your phone.

Identity Management and BTP: An Unexpected Requirement

Ask an SAP Basis or security team how they control access to their ERP, and you’ll get a confident answer. User master records, PFCG roles, firefighter IDs, separation of duties policies, and an audit someone reviews regularly. Decades of audit findings have hardened those controls. Ask the same team who can deploy code to their BTP subaccounts, and the answer is far from so confident.

Sentry under 8 mins

If you've never used Sentry, this is the short version. In under eight minutes, we walk through what it does, what it captures, and where to go next. Sentry is an application monitoring platform. It collects errors, logs, timings, and replays, and ties them together with tracing, so you can go from "something broke" to the line of code that broke it without opening six tabs. Here's what we cover: On data: out of the box, Sentry scrubs things like passwords, tokens, and credit card numbers. You decide what else gets collected, and you can choose US or EU storage.

Connecting Cloudflare and Honeybadger with OpenTelemetry

Honeybadger Insights is pretty great. It's even greater when you connect it to Cloudflare and start shipping logs directly from Cloudflare Workers to Insights. If you're already using Cloudflare to distribute your applications, it's easy to set up. Cloudflare Workers logs capture what your application is doing at the edge: console.log() output, uncaught exceptions, and system-generated logs for each request.

Measuring soft navigation web vitals

If we were to reduce web performance into a single term, it would be “web vitals”. Web vitals are a universal signal that every developer can measure, and more importantly, optimize for. They describe many facets of the user experience when it comes to performance, but they were limited to specific types of navigations. Capturing web vitals for your apps has been largely about users landing on your site from somewhere else. We measured that experience and you optimized for it.

Grafana 13.3 release: manage variables across dashboards, share findings with Notebooks, a UI refresh, and more

Grafana 13.3 is here, bringing more consistency and control to how you work across Grafana. Cross-dashboard variables help you manage shared dashboard controls from one place, while Notebooks let you capture and share findings from across Grafana in a single, editable document. You'll also get a first look at Grafana's biggest visual refresh since Grafana 7, along with new ways to guide and refine dashboards built with Grafana Assistant.

Network Optimization Guide With 10 Top Ways to Cut Slowdowns and Downtime

Why does everything crawl at peak hour, even though every switch looks fine? Calls break up and file copies stall. Then the tickets arrive, each one some version of "the network is slow." It's rarely one broken device. A WAN link maxes out for an hour with voice packets queued behind a backup job. Last week's config change hasn't been linked to any of it yet. Network optimization sorts this out one measured step at a time, starting with performance monitoring.

Default to serverless for AI agents, then let production data decide what moves

Serverless should be the first execution model teams evaluate for AI agent workloads. Many agents spend much of their lifetime waiting, and always-on compute forces teams to pay for every idle minute. In a study of more than 35,000 production coding-agent sessions, the median session ran LLM or tool calls for only 20% of its lifetime. The rest of the time, it sat idle between steps, with its sandbox still allocated.