Operations | Monitoring | ITSM | DevOps | Cloud

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.

A few months ago, Steve Yegge published his 8 levels of AI-assisted development, and it clicked the moment I read it, because I had lived that exact progression myself, moving from autocomplete to running agents one step at a time. Framed as an AI trust gradient, it finally gave the industry a vocabulary for something most of us were already going through without a name for it. If you haven’t read it, save it for later.

There's a version of this conversation that plays out in engineering organizations everywhere. Leadership pushes for standardization. Developers push back. The argument from developers is reasonable on its face: every codebase has different needs, every team has tools they're good at, and adding process feels like slowing down to go faster. It's a genuine tension, and it's also a false one. The teams that ship the most aren't the ones with the most infrastructure freedom.

The promise of agentic AI is dead simple to understand. Instead of waiting for a human to draft every instruction, an AI agent can interpret a goal, take action, and work across systems until the task is done. For IT teams, that motion sounds like the next logical phase of automation. That promise is real... but it’s also where the risk starts. Traditional automation followed instructions. Agentic AI, by contrast, pursues outcomes. That difference turns the entire governance model on its head.

How to choose one, and what options exist on the market.

‍ Learn how to master Azure deployment with CI/CD pipelines, progressive delivery, and feature flags. See how Harness helps engineering teams ship faster and safer on Azure. Azure deployment sounds straightforward. Push code, it runs in the cloud. But if you've managed a 2 a.m. production incident because a deployment went sideways on AKS, you know the gap between "it deploys" and "it deploys safely at scale" is significant.