Operations | Monitoring | ITSM | DevOps | Cloud

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

What is SRE Observability and Key Pillars You Should Know?

What happens when a critical service slows down, but nothing is technically “broken”? Most teams have monitoring in place. They know when something goes down. But when performance drops or issues spread across services, finding the real cause becomes slow and unclear. Engineering teams end up switching between dashboards, logs, and alerts just to understand what changed. This delays response and increases pressure on on-call teams. This is where SRE observability becomes essential.

It Can Only Goodhart Happen

When a measure becomes a target, it ceases to be a good measure. Charles Goodhart, 1975 You’ve probably read this quote in relation to any number of things over the years. People complaining about arbitrary metrics like PRs merged, lines of code produced, and now, token usage. But is the era of tokenmaxxing over before it even began? The rise of token leaderboards to the death of token leaderboards at companies like Amazon seem to have taken place in less than three months!

MCP Servers Are Becoming a Core Interface Layer in Data Observability and Data Quality

Data observability has traditionally been built around human workflows. When data breaks, engineers are alerted, open dashboards, inspect lineage graphs, and manually trace the issue across pipelines. The system is designed for human investigation and interpretation. That model is now being challenged by the rise of AI agents in data operations. As organizations begin embedding AI into analytics, engineering, and decision-making workflows, observability is no longer just about explaining what happened - it must also enable systems to understand and act on it.

Why Engineers Don't Trust Autonomous AI - 4th Annual Observability Survey | Grafana Labs

The 2026 Observability Survey from Grafana Labs heard from over 1,300 engineers and leaders across 76 countries on the real-world role of AI in observability. The data reveals a sharp distinction between intelligence and autonomy — and a critical blind spot most teams have.
Sponsored Post

How APM fits into the modern observability stack

Most engineering teams don't have a data problem. They have an interpretation problem. Prometheus is running, logs are shipping to the aggregator, dashboards are green-and then a latency spike hits and the root cause takes 45 minutes to isolate. The data was there but the answer wasn't. That gap is where application performance monitoring (APM) operates. This article explores what APM adds to a modern observability stack, why relying on standalone tools leaves critical blind spots, and how teams can unify infrastructure data with application context for a complete operational picture.

How to debug REST Collector APIs with Cribl REST Collector Diagnostics

This video introduces the new REST Collector Diagnostics feature in Cribl, which helps you troubleshoot API collection issues faster. It’s designed for observability and data engineers who use REST Collector to pull data from external APIs and need deeper visibility into HTTP requests, responses, and errors.

Claude Code Observability at Scale: How We Did It With Bindplane

At Bindplane, we iterate fast. One of the most important tools we've adopted across our organization is Claude Code. It helps every team here build solutions to complex problems with both speed and precision. But speed without visibility is a liability. We needed a reliable way to monitor and audit how Claude Code was being used across our team. Luckily, we build the best platform on the market for data in motion.

Why Observability Is Essential for Platform Engineers?

Observability is how platform teams stop being the answer to every question and start building platforms that answer those questions themselves. This article explains specifically how observability enables platform engineers to support development teams better which reducing ticket volume, cutting MTTR, enabling SLO ownership, and making microservice debugging something devs can do without escalating to you.

AI Observability Deep Dive Demo | Grafana Cloud

Grafana AI Observability is our new database and platform for observing AI Agents. Over the past year at Grafana Labs, we built Agents and we needed a way to understand how they are performing, what are the costs associated with them, what's the error rate or time to the first token as well as how they are behaving. Grafana Staff Engineer, Ivana Hučková provides a deep dive demo on how Grafana AI Observability connects our experience building Agents with our experience building observability systems.

Observability for Healthcare Systems | Grafana Everywhere

Grafana Assistant is going places you might not expect — including healthcare. Golden Grot winner Oren Lion from TeleTracking reveals how Grafana Cloud supports their systems that help keep patient care moving — and how Assistant enables teams to get from “what happened?” to “here’s why” faster. From moon landings to patient care, Grafana is everywhere. Congratulations to Oren, Chris Johnson, Mark Munson, and the entire TeleTracking team on winning this year's Golden Grot Award for Pioneering AI in Observability!