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

The Agent Runtime Needs an Enterprise Brain: Why Fabrix.ai Completes the NemoClaw / DefenseClaw Stack

The agentic AI security stack is taking shape , fast. At GTC 2026, NVIDIA unveiled NemoClaw, an open-source stack that wraps OpenClaw with enterprise-grade privacy controls, local inference via Nemotron models, and the OpenShell sandboxed runtime. Days later at RSAC 2026, Cisco launched DefenseClaw, an open-source governance framework that scans every agent skill, MCP server, and plugin before admission , and enforces block/allow policies at runtime with sub-two-second enforcement.

How to Reduce MTTR When Third-Party Services Go Down

Your on-call phone goes off at 3:17 AM. Payments are failing. You ssh in, check your pods — all green. Database? Healthy. Load balancer? Fine. You spend 22 minutes chasing ghosts before someone checks Stripe's status page and sees the incident that started 34 minutes ago. Those 22 minutes are pure waste, and they're exactly the kind of MTTR you can reduce without touching a single line of your own code. And the fix isn't faster debugging. It's recognizing that the failure wasn't yours to debug.

Telemetry Talks ep 3: OpenTelemetry with VictoriaMetrics observability signals

In this episode of Telemetry Talks, we explore OpenTelemetry observability signals—metrics, logs, and traces, and how VictoriaMetrics handles each of them with high performance, cost efficiency, and seamless integration. We briefly explain what each signal is, discuss common misconceptions, and share guidance on which signal to start with if you're new to observability. Together with our guests, both engineers at VictoriaMetrics, we walk through integrating VictoriaMetrics with the OpenTelemetry demo, showcase Grafana dashboards, and check the playgrounds for all three signals to see them in action.

Node Groups: Organize Your Infrastructure Into Reusable Views

When you’re managing a handful of nodes, the flat list in the nodes tab works fine. When you’re managing hundreds or thousands, it becomes a wall of hostnames. You end up applying the same filters repeatedly: all the production database servers, all the nodes in eu-west, all the Kubernetes workers in the staging cluster. The filters work, but they don’t persist, and there’s no way to share them with the rest of your team. Node groups solve this.

Unified Logging for a Single Source of Truth

In Star Trek, the Borg are a cybernetic alien organism that forcibly assimilates other beings and technologies into its hivemind called “The Collective.” Each assimilated being or technology becomes part of the unified consciousness, with the villainous Borg Queen as the leaders. As the only independent thinker, the Borg Queen leads this rapidly adapting Collective.

Agno Monitoring & Observability with OpenTelemetry and SigNoz

Learn how to implement end-to-end monitoring and observability for Agno-based AI systems using OpenTelemetry and SigNoz. In this video, we walk through instrumenting your Agno workflows, collecting traces, metrics, and logs, and visualizing everything in SigNoz to gain real-time visibility into performance, failures, and bottlenecks. You'll see how to move from basic logging to production-grade observability—so you can debug faster, optimize latency, and confidently run AI systems at scale.

Playwright Myths Busted: Speed, Flakiness, Production Monitoring & AI Test Generation

Playwright is too hard, too slow, and too flaky — right? In this webinar, Stefan busts six common end-to-end testing myths and shows how to reuse your Playwright tests as production monitors with Checkly. He covers codegen, trace viewer, UI mode, flakiness root causes (and fixes), and a quick look at Playwright MCP for AI-assisted test generation.

Checkly Playwright Reporter: A Cloud Dashboard for Your Playwright Tests

The Checkly Playwright Reporter is an npm package that sends the results of npx playwright test to Checkly as a cloud test session, including traces, screenshots, videos, and full debugging context. Run your Playwright suite in CI or locally, and every result gets a persistent, shareable home in Checkly with AI-powered analysis, richer trace-derived views, and a direct path to production monitoring. It does not replace Playwright. It makes the output of Playwright much easier to work with.

What Metrics to Monitor in Your Vibe Coded App

These days, using a tool such as Cursor, GitHub Copilot, Zed, or Claude makes it easier than ever to develop and deploy applications. You express your requirements, receive the completed project back as output, and there you have it! You now have an application that is in production and functioning. However, the surprise comes after the app has been deployed. When your app breaks or behaves abnormally, it may not be immediately obvious what is wrong or how to fix it.

Measure the business impact of every product change with Datadog Experiments

Modern product teams ship features constantly. Every change—whether it’s a new onboarding flow, pricing tweak, or UI adjustment—raises the same question: Did this improve the product? AI has changed the stakes entirely: As release cycles accelerate and code generation scales across every team, the volume of changes has outpaced most teams’ ability to measure their true value.