Operations | Monitoring | ITSM | DevOps | Cloud

We’re proud to announce that Coralogix has earned 196 badges across 15 categories in the G2 Spring 2026 Reports, our strongest G2 performance to date. Placing in 369 reports, this represents a significant leap from Spring 2025, when we placed in 318 reports and earned 141 badges. These results are a direct reflection of the trust our customers place in Coralogix and their willingness to share honest feedback on the world’s largest software review platform.

For mobile-first organizations, the distance between a “slow app” and a “resolved ticket” is often filled with guesswork. Mobile performance is notoriously difficult to capture because it lives at the intersection of device hardware, network stability, and local code execution. Today, we are closing that gap with the launch of Coralogix Mobile Performance.

At VictoriaMetrics, we built vlagent as a high-performance log collector for VictoriaLogs. To validate its performance and correctness under a real production-like load, we developed a benchmark suite and ran it against 8 popular log collectors. This post covers the methodology, throughput results, resource usage, and delivery correctness. Collectors under the test: We’ve made all benchmark configurations and source code public, so you can reproduce and verify the results independently.

How platform and SRE teams are using Mezmo's open-core agent framework — with any LLM, any tools, any observability backend.

I’ve been staring at Claude Code telemetry for the past few weeks, and I keep noticing the same thing: most teams drop it into their environment, say “it’s amazing,” and have absolutely no idea what it’s actually doing at the system level. That’s fine for a personal dev tool. It’s not fine when you’ve rolled it out to 50 engineers.

RSAC 2026 Conference is right around the corner, and we're unveiling new security capabilities at Booth N-5285. Book a 15-minute slot if you want an in-person walkthrough!

If you’ve worked with an observability pipeline, you’ve probably experienced schema problems: a field disappears, a type shifts from string to number, or a new label quietly appears. The causes are everywhere. Different teams adopt different naming conventions. A dependency upgrade changes the shape of a library’s log output. Over time, these small, reasonable decisions compound into schema sprawl: dashboards break, alerts misfire, and teams scramble to find out what happened.

TL;DR: Autonomous IT platforms combine observability data and AI to automatically detect, diagnose, and resolve issues—shifting operations from reactive monitoring to predictive, self-healing systems.