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The latest News and Information on Observabilty for complex systems and related technologies.

Instant Kubernetes Observability with Proxymock #speedscale #kubernetes #ebpf #devops #cloudnative

Jul 21, 2026 By Speedscale In Speedscale
Learn how to get instant observability into your Kubernetes cluster by installing the Speedscale operator and proxymock tool. In this step-by-step tutorial, we walk you through setting up the operator to capture live network traffic (including encrypted traffic, API calls, and database calls) without complex instrumentation or manual configuration.
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coroot

Let's break autovacuum in Postgres: reproducing failures to make it observable

Jul 21, 2026 By Nikolay Sivko In Coroot
Autovacuum is one of those Postgres background jobs that quietly keeps your database healthy. It cleans up the dead row versions that every UPDATE and DELETE leaves behind, and it keeps the database away from a hard transaction-ID limit that would take it offline. Most of the time you don't think about it, because it just works.
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harness

Introducing Harness AgentTrace: An Observability and Guardrail Framework for AI Agents | Harness Blog

Jul 21, 2026 By Sunil Gattupalle In Harness
AI agents fail differently from the software we spent the last two decades learning to monitor. We hear some version of the same story from teams shipping agents to production: an agent starts producing wrong answers. Not obviously broken: confident, well-formatted, plausible wrong. The logs are clean, latency looks healthy, and error rates sit at zero. Nothing flags a problem. A user eventually does.
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motadata

Security Observability: Pillars, Use Cases, and How It Works

Jul 21, 2026 By Poonam Lalani In Motadata
When an alert lands, does your team already see the full story, or does the work start with pulling scattered data together from one tool after another? For many organizations it's the second one, where the incident itself takes a backseat while analysts hunt across dashboards. The evidence is right there, scattered across platforms that don't share context. Security observability exists to close that gap.
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lightrun

SDLC Phases and the Reliability Gap AI Can't Close

Jul 21, 2026 By Lightrun Team In Lightrun
Decisions in each SDLC phase from planning to design, development, testing, deployment, and maintenance are made without sight of live production behavior. AI coding agents are widening that visibility gap faster, working faster than human engineers ever could. This piece maps exactly how this gap presents at each phase, and the harm that this brings.
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Adding Routing Intelligence To Your Observability Stack

Adding Routing Intelligence To Your Observability Stack

Jul 21, 2026 By OpsMatters In OpsMatters
Observability has a blind spot, and for most teams it sits at the network layer. You instrument your services, scrape metrics into Prometheus, ship logs somewhere searchable, and build dashboards that tell you when something is wrong inside your infrastructure. But the routing that carries traffic to and from that infrastructure often lives entirely outside the stack, watched through separate tools that do not talk to your alerting. This piece looks at why routing belongs in your observability pipeline and what it takes to get it there.
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grafana

Beyond performance monitoring: Understand the user experience with Grafana Cloud Frontend Observability

Jul 20, 2026 By Priscilla Lam In Grafana
You've optimized your Largest Contentful Paint. Your Time to First Byte is under 200ms. Your Lighthouse scores are green. And yet, your checkout conversion rate is quietly dropping. A segment of users in Southeast Asia is churning. Your support team is fielding tickets about a form that "just doesn't work" and you have no idea which one. Traditional frontend performance monitoring tells you whether your application is fast. It doesn't tell you whether people are actually succeeding when using it.
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motadata

What Is LLM Observability? A Complete Guide

Jul 20, 2026 By Ramya Shah In Motadata
If you run LLM features in production, your most dangerous failures are the ones your monitoring never flags. Your LLM feature passed every test, and the demo went great. Three weeks after launch, a support ticket lands: the chatbot quoted a refund policy that does not exist. The dashboards are all green, and the same prompt answers correctly when you retry it. This is the blind spot LLM observability exists to close. Your existing tools saw the request come back fast with a clean status code.
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