Operations | Monitoring | ITSM | DevOps | Cloud

Our bot is hitting an error. We can see it in the distributed trace. Here, see what happened when we noticed it: Austin fired up Claude Code (hooked up to Honeycomb with its MCP tool) and got it to find the error, fix it, deploy, and check that the fix worked. It got a little overconfident at first, but the ending is happy. IRL this took 22 minutes; the video speeds up the AI agent interactions and cuts out waiting. This video includes Austin Parker, Jessica Kerr, and Ken Rimple.

Check out just a few ways to bring data in to Honeycomb. This video highlights one-click AWS integrations, plus familiar log formats. Honeycomb Telemetry Pipeline Manager is an optional component of Honeycomb Telemetry Pipeline, a product available with Honeycomb Enterprise.

Integrate your AI agent with Honeycomb, and it can run queries to find problems, then get right into the code to fix it.

Right from your IDE, find out how long your code talked to execute in production. If your AI agent speaks Model Context Protocol, it can query Honeycomb for you!

Last Thursday, our /api/cart endpoint got a lot slower. But we didn't notice, because we hadn't set up a Service Level Objective to guard its reliability and responsiveness. This video shows how to identify and create that SLO in Honeycomb.io.

When your software integrates with Generative AI, you need great observability. You need to see everything about the interaction with the LLM. You also need to see everything around it! That's application observability, with distributed tracing.

Charity Majors, CTO and Co-founder at Honeycomb, and Phillip Carter, Principal Product Manager at Honeycomb, recently hosted a webinar with DORA's Nathen Harvey on AI's unrealized potential. As part of this, we created a 3-minute highlight reel of the webinar that you can watch.