Operations | Monitoring | ITSM | DevOps | Cloud

AI in observability at Grafana Labs: Making observability easy and accessible for everyone

Did you know that observability has been around for more than six decades? It all goes back to a Hungarian-American inventor named Rudolf Kálmán who thought about how external outputs could measure the internal state of a machine. Kálmán wrote about monitoring single-input single-output systems, but our demands are very different today. We need to observe monoliths, microservices, clusters, pods, regions, and many more.

AI for Grafana onboarding: Get your teams started quicker with Grafana Assistant

Grafana puts a powerful set of observability capabilities right at your fingertips, but onboarding entire teams to the sophisticated platform is often a nontrivial exercise—one that can slow adoption and prevent organizations from getting immediate value. We want to make the process as frictionless as possible, which is why we’re excited to tell you that Grafana Assistant is now available in public preview to all Grafana Cloud users.

Using Claude to power up your onboarding

I joined incident.io about ten weeks ago, having been in my previous role for four and a half years. Being a new starter was an unusual feeling for me, and there's been a huge amount to learn; but by lunch on my second day (!) I had started shipping value to our customers. A large part of hitting the ground running has been having a colleague alongside me, who I can pester with questions, who doesn’t get offended when I write in all capitals, and often praises me for being absolutely right!

Inside the Coralogix AI Center: Solving AI's Silent Failure Crisis

Observability has always answered one core question: Is it running? But in the era of LLMs, autonomous agents, and AI-powered workflows, that’s no longer enough. We need to ask a harder, scarier question: Is it right? And right now, most teams can’t answer that. Let’s fix it. In our last post, “The AI Monitoring Crisis No One’s Talking About,” we outlined why prompt injection, hallucinations, and context drift create invisible failures.

What Is an MCP Server?

Ok MCP server, If you’ve been following AI development lately, you’ve probably heard whispers about “MCP Servers” floating around developer circles. It’s been around a little while now, and I myself have finally gotten round to using it. Boy, do we need to talk about it. MCP (Model Context Protocol) is Anthropic’s open standard that lets AI assistants connect directly to your tools and data sources, not just static documentation or code snippets.

Getting Started with Grafana Cloud's AI Assistant for Observability

The pace of software delivery in 2025 is unprecedented — cloud-native apps, microservices, and AI-generated code are shipping in days, not months. But one challenge never changes: ensuring reliability and visibility when systems fail. In this video, we explore how the new Grafana AI Assistant brings true, context-aware observability to your stack. Watch as we deploy an open-source Python service with Kafka, Postgres, Kubernetes, and Prometheus then use the AI assistant to instantly generate dashboards, alerts, and reduce un-needed telemetry volume.

The PagerDuty Vision for AI-First Operations

Something fundamental needs to change in the way we run operations. Organizations are deploying AI to optimize everything from coding and deployment to resource planning and incident management. But they’re discovering that managing AI-powered systems requires a completely different operational mindset. AI models hallucinate. Data pipelines degrade silently. Algorithms develop bias without warning.

You built the MCP server. Now track every client, tool, and request with Sentry.

TL;DR - Starting today, you can instrument most server-side JavaScript SDK based MCP servers with one line of instrumentation code within your MCP SDK implementation. Click to Copy Click to Copy With this in place, you’ll be able to see details like protocol usage, client usage, traffic, tool usage, and performance across your MCP implementation.

AI Meets Mobile: How Companies Leverage Android and AIOps for Smarter User Experiences

Mobile devices are becoming smarter with every tap. Thanks to AI and AIOps, Android apps can now predict what users need, fix issues before they even notice them, and create seamless, personalized experiences. This isn't a distant tech dream but something that's happening right now, transforming the way companies design and deliver mobile services. From streamlining performance to enhancing customer engagement, AI is quietly rewriting the rules of mobile interaction.