Operations | Monitoring | ITSM | DevOps | Cloud

See how Honeycomb uses AI in our built-in assistant, Canvas. Then see how your agent can use Honeycomb with our MCP. Both can get from a vague question to the root cause of a latency spike in a few minutes, and the agent with MCP can even fix it!

Chapters: In this video we take a tour through Honeycomb's Frontend Observability offerings for Web and Mobile. We see how the launchpads can help spot performance errors, how errors that occur in the frontend can be traced all the way to their cause in other backend services easily with the error investigations feature, and how easy it is to find differences between traces across various devices.

Empathy is one of the superpowers of modern teams, especially when building tools that interact with humans. This talk by Kesha Mykhailov tells the story of Fin, Intercom's Customer Support agent, and how they transformed their approach to Fin's.

You can give AI agents everywhere fingers & eyes into your tool or service, by implementing an MCP (Model Context Protocol) server. It’s a great idea! It’s also a new kind of design and engineering. Jessica describes how it’s different from implementing an API or a GUI, and why it’s more exciting than either.

Did you miss Honeycomb's Observability Day San Francisco? Here are some highlights of the day.

Canvas is an AI-guided workspace inside Honeycomb that combines an AI assistant with an interactive notebook for visualizing query results and traces. You can ask a natural language question about your data and Canvas will immediately start exploring your traces, through multiple queries and other tools, to find the right next steps. Instead of having to write each query yourself, Canvas automatically proposes relational queries, comparisons, and visualizations that explain why an SLO fired or what changed after a deploy.

Modern teams face a persistent challenge: knowing when something goes wrong before their customers do. With architectures sprawling across dozens or hundreds of services, creating comprehensive alerting becomes an overwhelming task. You're left playing whack-a-mole with manual alert configurations, often missing critical issues or drowning in false positives. Today, we're excited to announce our solution to this challenge: Anomaly Detection (currently in alpha), Honeycomb's proactive approach to understanding and acting on service health.

In the months since we launched our public beta, we’ve been hard at work making Honeycomb MCP more useful and capable for agents and human operators alike. Our goal with this project has been, from the start, to allow AI to engage in the same kind of investigatory loops that we guide users towards. Many of the new features are designed expressly with this in mind, the most exciting of which is BubbleUp, now available in.

Did you know you can define a calculated field in your Honeycomb queries? You can, and with the power of Honeycomb AI you can ask it to write the calculated field definition for you. Find out how in this short video.

You can use your mobile tools to debug errors, but are you really looking at the root cause? With end-to-end observability, powered by Honeycomb's Mobile Android and iOS SDKs, you can see everything! We'll show you how to start from a mobile launchpad, view the errors, select a trace, and find that root cause.