Operations | Monitoring | ITSM | DevOps | Cloud

TL;DR: OrionIQ has launched the next generation of its Alert AI Analysis agent within the Open 360 AI platform, designed to automate and accelerate incident investigation. Key features of this evolution include: Agent-Based Investigation: Instead of relying on a single prompt, the system coordinates specialized AI agents to correlate data across diverse sources like logs, metrics, deployments, and tickets.

TL;DR: This is the engineering companion to our announcement post, Upgraded Alert AI Analysis: Automated Incident Investigation, read that one for what the new generation does for your team; read on for how it works under the hood. Interested in hearing more? Book a demo to see the Alert AI Analysis Agent live. Root cause analysis is one of the harshest tests you can give an AI.

This set of Redgate Monitor updates brings new AI-powered tooling into how teams work with their estate, alongside expanded coverage for Google Cloud SQL and Contained Availability Groups. Whether you're looking to get faster, insightful answers, plug Monitor into your own AI stack, extend monitoring to a new cloud platform, or keep job visibility intact through a failover, there's something here for you.

In the first half of 2026, we shipped a wide range of improvements across metrics, logs, traces, cloud, and the Kubernetes operator. Our main focus across open-source components and enterprise solutions was on performance, stability, and making observability easier to adopt and operate day‑to‑day. This roundup brings together the most important changes to date, including a quick look back at key anomaly detection improvements from 2025 that are now paying off today.

Infrastructure assessment tools scan your servers and estimate migration cost based on CPU, memory, and storage. They do not read your application code. Applications that appear migration-ready at the infrastructure layer may contain thousands of lines of code that block cloud adoption. Without code-level visibility, teams cannot make accurate 6R decisions.

When we launched Autonomous Worker Agents, the message we led with was simple: governance is inherited, not integrated. Agents don't get security bolted on after the fact. They inherit the OPA policies, RBAC, and audit trails already running your production pipelines. This post is about the layer underneath that promise: isolation. We let an Autonomous Worker Agent run shell commands and call APIs inside our pipelines.

A few weeks ago I went to quantify a trend I had already clocked: our test-writing had climbed, and kept climbing. Where a typical month once brought a handful of new tests, last month brought 436.

If you've been watching the Puppetlabs namespace on the Forge (or waiting on a pull request you opened) you've likely noticed that the pace of inclusion of community pull requests and fixes into the module releases has slowed over the last several cycles. That isn't how we want to serve the community that built these modules with us. The team has been reviewing how we can improve and this post is our commitment to do better. Here are the steps we're taking now: Back to top.

The numbers from the 2026 State of the Database Landscape: AI Edition are striking. 99% percent of respondents using AI report at least one measurable benefit for their database work. Automation is up, performance is improving, and three-quarters report significant cost savings. By almost any measure, AI is delivering. However, sitting alongside that near-universal positivity in the same dataset, security and privacy concerns have climbed to 64%. Regulatory compliance anxiety has risen to 40%.