Operations | Monitoring | ITSM | DevOps | Cloud

AI is changing how we build software — but debugging code still comes down to having context. One minute the model’s performance is cruising. The next, you’re hit with a KeyError from a tool you forgot existed, triggered by a model that silently timed out, and a retrieval call that returns... nothing, or 11 “Let me try this a different way" messages before failure. You’re stitching together LLM calls, agents, vector stores, and custom logic. Then hoping it holds up in prod.

If your legal department is anything like ours, you’re being inundated with requests from the business to use more and more AI tools. Whether it's developers wanting to use coding agents like Cursor, to security implementing AI-driven investigations, to sales and marketing leveraging AI for call insights and competitive research, we've seen a shift in what teams are trying and buying.

You deploy a fix late Friday and spend the weekend refreshing dashboards, hoping nothing breaks. You shouldn’t have to babysit a dashboard to know when something’s wrong. With the latest updates to Insights, you can now create alerts directly from any chart. Whether it’s a spike in 4xx errors after a deploy, a jump in P95 latency for an API endpoint, or a drop in throughput for a background job, you can set up alerts with just two clicks.

You write code. Open a PR. CI runs. PR merges. Prod’s on fire by 5pm. Maybe you skipped writing some tests. (It's tedious, sometimes unclear, and easy to ignore when you're racing to ship—until something breaks and you realize a test could’ve saved your Friday night.) Maybe the PR review was more of a drive-by from a teammate who barely had time to skim the diff. But reviews and tests matter.

What do the Snapchat, Airbnb, and Spotify iOS apps have in common? They all use order files to speed up their iOS app launch times. Order files re-order your binary to improve how symbols are loaded into memory. No code changes are necessary, but generating an optimized order file can be cumbersome, so it’s mostly done by larger teams or teams willing to pay for a service like Emerge Tools’ Launch Booster. It just so happens that Emerge Tools is now part of Sentry.

Tired of trying to guess if that half-baked LLM suggestion is really going to fix the issue with your code? Meet Seer—our new AI agent that taps into all the issue context from Sentry and your codebase to not just guess, but root cause gnarly issues and propose merge-ready fixes specific to your application. Code gen tools are great fun—and useful. But even a recent Microsoft study confirmed what you already know: AI struggles with debugging.

If you've ever had to debug a Flutter app after an error report that just says “Null check operator used on a null value,” you already know: context is everything. And context can be hard to come by when you’re juggling native code, Dart, async stack traces, and platform channels. With v9 of our Flutter SDK, we’re introducing some features to help you get even more visibility into what’s going wrong, with the insights to make it better. Here’s what’s new.

It’s one thing to debug your game during development, but once your game is in production, visibility into errors and failures in real gameplay is harder to achieve. And when something does go wrong, players are more likely to ragequit than submit a bug ticket.

Sentry’s auto-instrumentation, using BrowserTracing, is convenient. You can get interesting insights about your frontend application out-of-the-box, such as whether slow and failing API calls are hurting your user experience (summarized in Network Requests), or how your website stacks up against industry standards for performance (summarized in Web Vitals).

Unless you’ve been living under a rock, “MCP” is probably a term you’ve heard thrown around in the AI space. Each of the editors and LLM providers have been racing to add and enhance their MCP support. Sentry was fortunate enough to be included in Anthropics release announcements for MCP.