Operations | Monitoring | ITSM | DevOps | Cloud

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.

Lets be real, who really knows whats happening inside MCP Server? Well, now you can! We've been building our MCP Server for the past few months, and we've taken all the tools that we've used to debug it and turned them into a set of observability tools to help you monitor your MCP servers end to end.

We just launched MCP server monitoring in beta. You can instrument most server-side JavaScript SDK based MCP servers with one line of instrumentation code within your MCP SDK implementation using: wrapMcpServerWithSentry(McpServer) See details like protocol usage, client usage, traffic, tool usage, and performance across your MCP implementation so you you can get visibility into all the sharp edges that your MCP server has — who’s using it, how it’s working (or not), and get alerted when things break.

Build an MCP server starting from 0 using Vercel's Next.js template, and ends in generating hot takes in the tone of David Cramer. Scaffold Sentry, add tool calls via Cursor CLI, and setup MCP monitoring with Sentry's new wrapMcpServerWithSentry method.

Learn how to set up the Cortex MCP in under 5 minutes. The MCP integrates directly into your IDE, giving instant access to Cortex data without leaving your coding environment. It reduces context switching by enabling natural questions about services and teams, and streamlines workflows with real-time data from Cortex, Jira, GitHub, and more.