Operations | Monitoring | ITSM | DevOps | Cloud

Enterprise AI struggles not from inadequate models, but from fragmented data architecture. Critical business data remains trapped in legacy systems or lost in streaming complexity. Success requires bridging the gap between modern intelligence layers and underlying systems of record.

IPoDWDM is an architecture that integrates IP routing and Dense Wavelength Division Multiplexing into a single, converged platform. This integration is achieved by placing coherent optics directly into the ports of IP routers and switches, a fundamental shift from traditional network designs. Consequently, this approach eliminates the need for a separate, dedicated layer of optical transponders and their associated shelving.

We have been getting the same request from teams for a while now: “We use Scout for our Rails app. Can we get the same thing for our Node services?” Today the answer is yes. Scout Monitoring now supports Node.js. If your team runs Express or NestJS in production, you get the same errors-and-traces experience that Ruby, Python, PHP, and Elixir teams have had. Let’s walk through what that means in practice.

You can use Grafana Assistant Investigations to automatically discover incidents and help find root causes—and this AI-powered Grafana Cloud feature recently got a major upgrade to give you even more confidence in its findings. You can read more about the behind-the-scenes effort in our new engineering blog Unprompted, where we get into harness engineering, context compaction, benchmarking, and keeping agents alive and working well in long-running sessions.

MSPs already have plenty of tools. The harder problem is getting a clear read on what’s happening across each customer environment, which alerts point to the same issue, and where engineers should start. Choosing the right MSP software is really about choosing the right operating layer for service delivery. MSPs are supporting more customers, more environments, and more alerts, but adding another tool doesn’t always make the work easier.

Something unusual happened in the first half of 2026: the most productive AI coding tool on the market became the most financially dangerous. And the companies that discovered this the hard way read like a Fortune 50 roll call.

Your engineers run Claude Code. Your designers are in Cowork. Half the company has Claude open in a browser tab, and a few are on Cursor. It’s on their laptops, each person authenticated a different way, and none of it touches your gateway. The only record you get is one lump-sum bill at the end of the month. Now you can capture it where it happens – on the laptop.

This edition of the Pulse is shifting lanes. We’re calling it the AI Economics Pulse now, because the question on every finance leader’s mind is whether AI spend and the returns on it can be made to pair at all. That question came to a head over the last few weeks. The bills came due, and they came due in public. Uber burned through its entire 2026 AI budget in four months and capped employee spending on Claude Code and Cursor at $1,500 a month.

AI changed how code gets written before it changed how code gets operated. Generation accelerated; the downstream controls that turn that output into reliable, secure software at a reasonable cost did not keep pace. The result is elevated risk, distributed unevenly across engineering organizations. A recent survey explains why the distribution is so uneven.

If you're sending security data to Microsoft Sentinel, you now have a faster path. A new ASIM mode lands your logs directly in Sentinel's native ASIM tables: no custom tables to predefine, no schema to design before data flows. We added ASIM mode to the Microsoft Sentinel destination, backed by a new ASIM standardization processor that converts raw logs to ASIM in the pipeline and routes each record to the table it belongs in. Here's how it works, and why we built it this way.