Operations | Monitoring | ITSM | DevOps | Cloud

Building a catalog used to be a project. It meant months of tracking down owners, untangling dependencies, and manually piecing together a picture of your architecture. It was a tedious, thankless process that delayed the value of your Internal Developer Portal (IDP) before you even got started. Now, it’s a coffee break. We’re excited to introduce Magellan, our new AI-powered data engine designed to build your catalog and get your IDP live in minutes.

Here's a scenario every on-call engineer knows too well: a critical incident fires for a service you’ve never seen before. Your first ten minutes are a frantic scramble across wikis and Slack channels just to answer the most basic questions: Who owns this? What does it do? Where are the runbooks? By the time you’re oriented, the incident has escalated.

AI is transforming how software is written and operated. Every day, engineering teams are discovering new ways to accelerate development, reduce toil, and push the boundaries of innovation. But this acceleration makes it easy to forget a fundamental truth: speed without guardrails creates risk, especially when implementing the AI-powered tools that dominate today's news cycles.

Over the last few months, I’ve had countless conversations with my peers about one topic: the rise of AI coding assistants. I know this isn’t exactly breaking news, and I’m sure you’ve had these conversations as well. But there’s a reason the common coffee chat today is 10 percent small talk and 90 percent about the AI-first world that we live in. Tools like GitHub Copilot, Cursor, and Devin are fundamentally changing how we write software.

Your production checkout flow just started returning 500 errors. Six microservices handle checkout. Logs show errors in three of them. Which service broke? Which error happened first? What caused the cascade? Traditional debugging doesn't work. You can't attach a debugger to production. Searching logs across six services gives thousands of lines with no obvious connection. By the time you correlate timestamps and trace IDs manually, customers have abandoned their carts.

As a platform integrator, we get a unique look at the tools our customers adopt every day. Of all the shifts I’ve seen, none has been as rapid as the adoption of coding assistants. The conversation has quickly gone from ‘is this tool really going to drive value?’ to ‘how quickly can we roll this out?’ No one can doubt the immense value these tools provide in shipping code faster.

We are thrilled to announce that the Cortex Model Context Protocol (MCP) is now available in the Devin marketplace. This integration connects the world’s first AI software engineer with the real-time context of your entire engineering ecosystem, as managed and measured by Cortex. The rise of AI software engineers like Devin fundamentally changes how organizations tackle their biggest technical challenges.

We have launched Request Mirror, a little free service to reflect HTTP requests. We've also open-sourced it: you can read the code in the ohdearapp/request-mirror.ohdear.app repo on GitHub. In this blog post I'd like to explain why we built it and how you can use it.

If you've adopted DORA metrics as your standard for measuring DevOps performance, stop us if this hypothetical scenario doesn't sound familiar. You check your DORA dashboard during a lunch break, full of optimism that you’ll get a clear picture of your team’s performance. Instead, you leave with nothing but a sandwich in your stomach and the nagging feeling that you’re focusing too much on the results of the game instead of the people that are playing it.

For most engineering leaders, getting a DORA dashboard up and running feels like a huge win. You can finally track performance, compare it to industry benchmarks, and report on your progress. But then a nagging question settles in: how do you actually make the numbers go up? That frustration points to a common gap between the dashboard and the daily engineering practices that drive those outcomes.