Operations | Monitoring | ITSM | DevOps | Cloud

About a year ago, Honeycomb kicked off an internal experiment to structure how we do incident response. We looked at the usual severity-based approach (usually using a SEV scale), but decided to adopt an approach based on types, aiming to better play the role of quick definitions for multiple departments put together. This post is a short report on our experience doing it.

With every job I have, I come across a new observability tool that I can’t live without. It’s also something that’s a superpower for us at incident.io: we often detect bugs faster than our customers can report them to us. A couple of jobs ago, that was Prometheus. In my previous job, it was the fact that we retained all of our logs for 30 days, and had them available to search using the Elastic stack (back then, the ELK stack: Elasticsearch, Logstash, and Kibana).

If you want to know exactly what’s on your network and how it’s all connected in real time, then network observability is the answer. Network observability pulls data from sources across your network infrastructure to model a detailed view of your systems and how they interact. This lets you understand exactly what’s happening on your network at any given moment so you can optimize performance.

Modern observability platforms are transforming how organizations handle application performance monitoring and troubleshooting.

Earlier this year, we introduced relational fields. Relational fields enable you to query spans based on their relationship to one other within a trace, rather than only in isolation. We’ve now expanded this feature and introduced four new prefixes: child., none., any2., and any3.. Previously, you could use root., parent., and any. to query on the root span of your target span’s trace, the parent span of your target span, and any other span in the same trace as your target span.

SolarWinds Day has consistently been one of the most enlightening events of the IT year, offering rich insights into technology, cybersecurity, artificial intelligence (AI), and more. This quarter's event, SolarWinds Day: Observability Anywhere. Precision Everywhere, tackled the complexities of IT infrastructure observability. I was delighted to host the panel discussion; here’s my overview of the key talking points.

Infrastructure monitoring has evolved into a critical component of modern distributed systems, driving organizations to explore robust Zabbix alternatives. While Zabbix has served as a cornerstone of traditional monitoring, today's microservices and cloud-native architectures demand different approaches. The landscape of Zabbix alternatives has matured considerably, offering specialized solutions for various monitoring scenarios.

In the first part of this series, we explored how AppSignal can significantly enhance the robustness of Open edX platforms. We saw the challenges that Open edX faces as it scales and how AppSignal's features — including real-time performance monitoring and automated error tracking — provide essential tools for DevOps teams. Our walkthrough covered the initial setup and integration of AppSignal with Open edX, highlighting the immediate benefits of this powerful observability framework.

Logz.io introduces its AI Agent in Beta, using GenAI to revolutionize observability. The AI Agent simplifies monitoring with automated data analysis and root cause detection, accelerating issue resolution by 3-5x for beta users—marking a critical step toward fully autonomous observability.