Operations | Monitoring | ITSM | DevOps | Cloud

What the 2025 DORA Report Teaches Us About Observability and Platform Quality

The 2025 DORA State of AI-Assisted Software Development Report delivers a critical insight for technology leaders: AI is fundamentally an amplifier, not a solution. It magnifies the strengths of high-performing organizations with robust observability while exposing the dysfunctions of struggling ones. For organizations that have rushed to adopt AI coding assistants all while expecting immediate productivity gains, this finding demands a strategic pivot.

Honeycomb Observability Day SF - Kesha Mykhailov, Fin.ai: Human-Centric Observability in AI Systems

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.

How to Responsibly and Effectively Contribute to Open Source Using AI

With the influx of AI tooling, it’s never been easier to contribute to open source communities. These tools are capable of gathering context quickly, “understanding” repositories faster than ever before. They provide instant summaries about repositories that, previously, would have meant reading lines and lines of code. They can fix bugs in programming languages you don’t know, and ultimately allow more contributors to get involved, which (almost) every open source project wants.

Integrating JMX and OpenTelemetry

The OpenTelemetry community and the contributors to the Java Special Interest Group (SIG) have spent a great deal of time integrating core Java technologies into the project. An integration that is particularly useful is Java Management Extensions (JMX). It has been around since J2SE 5, and has been mature for some time. Many of the most widely used Java applications have adopted it over time and support this extension.

Observability Day San Francisco: The Future of AI and Observability Is Bright

AI and observability are no longer separate conversations—they’re deeply intertwined. Across keynotes, panels, and demos, speakers at Honeycomb's Observability Day San Francisco unpacked what that means for engineering teams today: faster insights, smarter tools, and new challenges to solve.

Introducing Honeycomb Intelligence Canvas

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.

Meet Canvas: Your AI-guided Workspace Within Honeycomb

Modern systems are wonderfully capable, but relentlessly complex. Debugging across microservices, frontends, and cloud edges often means switching between five or more tools, trying to stitch together “what changed” and “why it broke.” Honeycomb’s wide events model has proven to be a superpower for taming that complexity, by allowing you to easily observe and query end-to-end traces without worrying about how much granular data you attach to your events.

Introducing Anomaly Detection: Your Early Warning System for Service Health

Modern engineering teams face a persistent challenge: knowing when something goes wrong before their customers do. With microservices 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.