Operations | Monitoring | ITSM | DevOps | Cloud

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.

A credible FIPS 140-3 transition does more than replace an operating system. It protects authorization timelines, service continuity, rollback options, and accountability across the operational boundary.

If you were anywhere near LinkedIn last week, you probably saw us launch AI Signals. We weren’t exactly quiet about it. (Press release, a couple of blog posts, and more social posts than we’d like to admit. Sorry about your feed.) We covered the why behind AI Signals already, but I wanted to actually walk you through what you’re seeing on the screen. Sooner or later someone asks what the company spent on AI last month.

Apache Kafka's documentation and operational lore for KRaft are clear on one point: you shall never lose controller quorum. It is good advice, because a Raft quorum that can no longer elect a leader cannot commit metadata, and a Kafka cluster without a functioning controller cannot create topics, change ACLs, elect partition leaders, or otherwise make progress on its control plane.