Operations | Monitoring | ITSM | DevOps | Cloud

If you've never used Sentry, this is the short version. In under eight minutes, we walk through what it does, what it captures, and where to go next. Sentry is an application monitoring platform. It collects errors, logs, timings, and replays, and ties them together with tracing, so you can go from "something broke" to the line of code that broke it without opening six tabs. Here's what we cover: On data: out of the box, Sentry scrubs things like passwords, tokens, and credit card numbers. You decide what else gets collected, and you can choose US or EU storage.

Sentry offers both "Application Metrics" and "Logs", but both can seem pretty similar. Both logs and metrics are just a collection of attributes attached to a name in your code. Both can be counted, visualized, or alerted on. Both are not sampled by default. So how do you know which to choose? In this video, we'll briefly go over a few examples of when to use logs and when to use metrics. TLDR; Logs are designed for debugging and may be temporary or likely to change when problems arise. Metrics on the other hand are stable, purposely placed events used to track user behaviors.

Recently Warp introduced some new cloud software factories for setting up bespoke workflows and agents within your organization. The best part is that you can integrate Sentry directly into these factories. Whether you want to create an automated debugging workflow leveraging Sentry context or capture agent metrics for evals and performance data, Warp and Sentry have got it covered.

Use Sentry tracing to investigate three different problems in one shopping flow: an N+1 query, an expired promo-code warning, and a checkout exception. The entire shopping flow happens through the agent conversation on Empower Plant’s homepage: a search for plants under $200, adding one of each to the cart, starting checkout, applying SAVE20, then attempting to place the order. The trace connects that flow to repeated database queries and an N+1 issue, a warning that SAVE20 has expired, and a checkout exception with its inventory-validation stack trace.

Join Serge for an introduction to tracing and how it applies to AI agents. Through live walkthroughs, you’ll see how to follow an agent’s execution, inspect model and tool calls, and find where things went wrong. No prior tracing experience is needed.

We use Sentry's new Regex search for Logs to hunt for non-obvious bugs in our app. What do you do when traces show that Postgres jobs are backing up? Logs are trace-connected, so by looking at one of the affected traces, we can see all of the logs on that trace. In there, we can see Postgres has logged that it acquired a lock only after a long wait. Using regex, we can find all of these logs that describe acquiring a lock after a 10-second-plus wait. That narrows our search from thousands or hundreds of logs down to about a dozen.

This video walks through what it looks like to treat Sentry's Seer agent the way you already treat a chatbot, asking it plain language questions about your own production application. From Slack or inside your Sentry account, Seer Agent can answer questions with real context: the affected spans, the recent release, the likely cause. Seer doesn't stop at answering questions. Ask it to act on what it just told you and it completes that work inside Sentry, serving as an assistant to your projects, team workflows, and your future self.

Welcome to HotFix, the show where Sentry customers return the favor. We helped Exa fix their code. Now, it's their turn to fix our organization.

Checkout suddenly got a lot slower for a chunk of users, and the only clue was a spike in an application metric. In this walkthrough, we go from that spike to a linked trace that shows exactly where the time went. Using Codex alongside Sentry, we dig into the trace data, isolate the slowdown to the European backend, and follow it down to a single Stripe request that's dragging everyone down with it. Along the way, we talk through what actually helps here: caching, timeouts, async calls, and other ways to keep one slow dependency from becoming everyone's problem.

As fast as AI can generate code, getting that code to production still tends to slow things down. It makes sense. When code breaks in production it’s not just a nuisance; it’s a full-blown incident. And can have big impacts for your company and customers. The question really is, can you catch broken code as quickly as you can ship it? In this livestream, Peter McCarron, Technical Product Marketing at Sentry, is joined by Tom Totenberg, Head of Release Automation at LaunchDarkly, to talk about how you can automatically protect new releases from code merge to error detection.