Operations | Monitoring | ITSM | DevOps | Cloud

Until now, someone on your team had to start every root cause analysis in Rollbar. An alert fires, they open the item, read enough of the stack trace to decide it deserves an investigation, and start the analysis by hand, after dropping whatever they were doing. With Agent Automation, Resolve, Rollbar’s AI agent for root cause analysis and fixes, takes over that step. You write a rule once in Project Settings, and when an error matches it, Resolve starts the analysis on its own.

The first CRA deadline is behind us. Now comes the opportunity to solve the engineering problem underneath it. When I worked with connected appliances at Electrolux, I learned something that is obvious to customers but sometimes less obvious to product organizations: Release day is not the end of product development. In many ways, it is the beginning of the longest and most unpredictable part of the product lifecycle.

The digital workplace has outgrown the support model built to maintain it. Employees now depend on an mix of devices, applications, AI tools, and connected workflows to get their work done. As this environment becomes more complex, IT is expected to support more technology, control costs, and enable greater productivity. Traditional, ticket-driven support makes that difficult.

Most of the time, when we read a file, we do not think much about what happens underneath. You ask for some bytes, you get some bytes. But when getting data off the disk efficiently is at the core of what your application does, what happens underneath starts to matter a great deal, and two identical read() calls can differ enormously in what they cost. So in this post we are going to look at one of the Linux kernel’s optimizations for reading files: readahead.

If you’re running FastAPI, you’re running Starlette underneath it, whether you think about it or not. At Scout, we spend a lot of time thinking about what happens at that ASGI layer, since that’s exactly where our Python agent hooks in to give you request-level visibility. Starlette 1.7.0 just landed, and it’s a release worth reading closely if you care about tracing, routing, or just keeping your app running on a supported dependency chain.

A few months ago, we launched Agent Timeline to close the gap between knowing an agent failed and understanding why. It took the tangled reality of a multi-agent, multi-trace workflow and rendered it as a single, readable conversation: every LLM call, tool invocation, handoff, and downstream system span laid out in the order they happened.

Earlier this year, we introduced more capabilities to support agents in production, a more chaotic, complex environment that requires a tremendous amount of context to understand. Unlike tools that capture shallow, pre-aggregated metrics, or cannot join a metric, trace, and log in one query, Honeycomb retains the context and connective tissue from telemetry data to build a nuanced view of production.

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.