Operations | Monitoring | ITSM | DevOps | Cloud

Here's a scenario that's playing out in engineering teams across the industry right now. A developer uses AI to rapidly prototype a microservice. The code works. They deploy it to production. Six months later, something breaks. The system is under load, a database connection pools, and the service starts failing in subtle ways. The engineer pulls up the code, but here's the problem, they didn't write it. An AI assistant did. They don't understand the flow deeply. They don't know where to look first.

Whether you're using SolarWinds, PRTG, Datadog, or another long-standing monitoring solution, chances are your environment has evolved significantly since the platform was first deployed. New applications have been added. Infrastructure has expanded into cloud environments. Teams have developed custom dashboards, reports, alerts, and workflows. Over time, monitoring becomes deeply woven into daily operations. That's why many organizations continue using tools that no longer meet their needs.

Agentic coding tools like Claude Code can write, refactor, and debug across an entire codebase, but by default they read code as plain text, the way grep does. The Language Server Protocol (LSP) changes that: it’s the same code-intelligence layer an IDE uses, and wiring it into an agent lets it read code by meaning instead of by string match. The bigger the codebase, the more a wrong guess about a symbol costs, and the more that structural view pays off.

The core of ROI is visibility. If you can clearly see … 1. What it costs to produce the thing you make, and 2. How much money it makes you … then calculating ROI is easy. But with AI, as with the cloud before it, getting that visibility is extremely challenging. Why? Because the cost data associated with each is inherently chaotic.

CloudZero Explorer now tracks your query history and lets you save up to 12 configurations and 10 favorite dimensions, so your most-used cost analyses are one click away.

The Preservation Hold Library silently inflates SharePoint storage. Replace it with Squirrel Recycle Bin Capture for compliant retention in customer-owned Azure.

Today, we’re launching Kubex support for the KAI Scheduler and automated GPU sharing for inference workloads. As AI inference moves into production, platform teams are being asked to serve more models, support more teams, and control GPU costs at the same time. But many inference workloads do not need an entire GPU all the time. When teams reserve full GPUs or oversized GPU fractions to stay safe, expensive capacity can sit idle across the cluster.

Eight buyer questions for SharePoint archiving 2026. Compares Microsoft 365 Archive, AvePoint Opus, Archive360 and Squirrel on data, Copilot, labels.

Machine learning models are not like other software artifacts. A single fine-tuned LLM can weigh 70 GB. A model family may share 95% of its weights across dozens of variants. When hundreds of developers, training jobs, and GPU clusters all need the same model at the same time, the infrastructure underneath needs to be built for it.

In November 2025, we introduced new triggers and workflows to Bitbucket Pipelines to help teams manage and scale complex CI/CD workflows. We later extended that foundation with additional event-based triggers for pipeline, deployment, and pull request events. We’re now extending that model with a new package-artifact-created trigger.