Operations | Monitoring | ITSM | DevOps | Cloud

Big improvements to Seer Agent

Today marks exactly five months since we launched Seer Agent, our friendly AI agent for querying all things in your Sentry projects (well, five months and a day ago, but close enough). Since then, we’ve had a lot of chats with Seer Agent. You may have even seen a few of them popping up on social media recently. It’s become an integral part of our debugging workflows and feature iteration process.

Protecting releases, fixing bugs with Sentry & LaunchDarkly

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.

Building Sentry's Laravel AI Integration

During a recent Agent Hackweek, an internal Sentry event that gives us a week to build any AI or agent project we want, a colleague pitched me on writing the Laravel AI integration. The goal was to give agents built with Laravel AI the same Agent Tracing support we already have for other frameworks. I liked the idea, he built Sentry’s Agent Tracing for Python based agents before which meant he already had domain knowledge.

Debugging our AI search assistant with agent tracing

In order for users to get the most out of the data being sent to Sentry, it’s important that we make it easy to find that data. Our team works on features to help users browse their data to find a particular event using search queries and filters. The search bar enables users to find their data by specifying search terms. Searching uses the Sentry Search Syntax, which can be barrier for users.

Application Metrics caught my broken size estimator

There’s a very specific kind of frustration that comes from waiting several minutes for a video to encode, dragging it into a message, and getting hit with a “file too large” error. Then you’re blindly trying to shave off a few more megabytes by re-encoding, maybe at a lower resolution or a smaller bitrate, hoping you won’t have to do it more than one or two more times. Here’s how I used Sentry’s Application Metrics to make a more accurate video size estimator.

From one switch to a control panel: meet `dataCollection`

This post and its code examples focus on the JavaScript SDKs. If you’re on another platform, it’s still worth reading to understand why we made the change and what’s coming your way. We’re replacing the boolean sendDefaultPii with a new option called dataCollection. The old switch was all or nothing: turn it on and you got everything, leave it off and you got only a fraction.

Debug AI agents wherever they run, from Slack bots to code review with Sentry's Agent Tracing

Agent Tracing shows the full execution path of an AI agent: the model call, every tool invocation and its arguments, token counts, cost, and the span where it broke. Same traces and spans you already use, with agent-specific attributes on top. Serge walks through three apps — a Next.js e-commerce agent using the AI SDK with a failing tool call, a Slack bot built with Eve that orders lunch, and a code review agent built with Flue over MCP.