Operations | Monitoring | ITSM | DevOps | Cloud

You don't need to pick one: how Sentry and OpenTelemetry work together

You already instrumented the backend with OpenTelemetry. Your services emit spans. Your teams know the OTel APIs. Maybe you already run a Collector. So when you start evaluating Sentry, the obvious question is: Do you need to replace your OpenTelemetry setup with the Sentry SDK? No. The practical answer is usually: keep OpenTelemetry where it already works, add the Sentry SDK where it gives you more application context, and send OpenTelemetry Protocol (OTLP) events to Sentry.

Your agent can't fix what it can't see

Agents are getting better and better at fixing bugs. They’re even getting better at testing their work, thanks to headless browsers, sandboxes, simulators, etc. But what about the bugs that only show up once you bring in different browsers, languages, extensions, internet speeds, and all the other variables that get mixed in the second you ship to prod? Or all the bugs that only show up when you account for… well, humans being humans and doing weird stuff you didn’t expect them to do?

The product analytics you already have

You already have everything you need. If you’re using Sentry, you have traces, structured logs, and now application metrics. Most teams use that stuff for debugging and stop there. But get this: that same data can answer most of the product questions you’ve been sending to a separate analytics tool, maintained by a separate team, with a separate data model and a separate bill. (Not all of them.

New ways to agentically build and edit dashboards

The traditional dashboard workflow, teams slowly handcrafting visualizations to track critical KPIs, is dying in a world of AI agents. A few years ago, in a pre-agentic-everything world, we tried to make it easier for developers to monitor critical experiences. We introduced Insights pages, which were pre-configured dashboards any Sentry user could adopt instantly that surfaced common health signals, like Web and Mobile Vitals.

Getting Started with XcodeBuildMCP: Let AI Agents Debug Your iOS Apps

XcodeBuildMCP gives AI agents the ability to build, test, and debug native iOS and macOS apps. In this hands-on workshop, we show you how to use the open source MCP server to unlock the full developer loop — build, run, debug, interact, and verify — without leaving your preferred AI coding environment.

From vibe code to production-ready: observability for Next.js and Supabase apps

The way we build software has drastically changed over the past few years. What hasn’t changed is that this software ends up in front of real people: you, me, my mom. And when those users inevitably run into something broken, you as the application’s developer need to be equipped with the right tools, context and understanding of what broke, where it broke, and how to fix it as quickly as possible. Every day we’re inching closer to self-healing software.