Operations | Monitoring | ITSM | DevOps | Cloud

TL;DR Choosing the right AI-powered observability platform isn’t about who has the most AI features. It’s about which platform helps your team identify root causes faster and spend less time investigating incidents. Here’s the short version: Logz.io + OrionIQ: Autonomous AI agents investigate incidents, perform root cause analysis, and surface next steps. Open standards, Kubernetes-ready, and deploys in as little as a week.

Making custom Elastic integrations better with both Automatic Import improvements and Integration Skills Elastic 9.4 shipped two tools for building custom integrations: Both are available today.

OpenTelemetry won the architectural standards battle. At scale, though, telemetry breaks more like plumbing than code. It breaks quietly, across a graph, with a blast radius you don’t understand until it’s expensive. With over 65% of organizations now running more than 10 collectors in production, hybrid deployments across Kubernetes and VMs are accelerating fast. Telemetry standardization is no longer a project milestone. It is a baseline expectation.

The race to better AI-assisted observability has been a race for bigger and better models. But intelligence was never the real bottleneck. Structure and context were.

How many of your last five incidents were already sitting in the logs before anyone noticed? Most teams already collect more than enough log data. The problem starts with what happens next, and the same four gaps show up almost everywhere: This guide covers the log monitoring best practices that close those gaps. It walks through how to collect, structure, correlate, retain, and secure logs, so monitoring becomes a steady process and not a scramble during the next incident.