Operations | Monitoring | ITSM | DevOps | Cloud

To change your engineering culture, start by asking your team what sucks

Most engineering leaders have a very known and very annoying "normal error." It's the log entry or deployment glitch that has been around so long that it is simply accepted as part of the status quo. Jeff Schnitter, a Solution Architect at Cortex, describes this as a form of organizational Stockholm syndrome. This mindset is unsustainable for several reasons.

Recapping our webinar on the Engineering in the Age of AI: 2026 Benchmark Report

I remember the first time I used an AI coding assistant. I watched the cursor dance across my screen and generate a hundred lines of code in seconds. It felt like I had finally found a cheat code for software engineering. That initial rush of productivity is a dopamine hit that's intoxicating and makes you think you can do anything with just a simple prompt or two.

Feature Friday: Personalized Context in One Click. New Cortex MCP My Workspace

Stop digging through tabs and explaining your role to your tools. In this Feature Friday, we’re unveiling 'My Workspace' for the Cortex MCP, designed to give you instant, personalized identity the moment you start your day. Before today, you had to manually fetch data from Jira, GitHub, and internal docs just to figure out your priorities. Now, you can simply ask, "What should I work on this week?" and get a parallel, high-speed pull of your entire ecosystem.

How AI amplifies your entire engineering culture

Anyone who has ever attempted to learn the guitar knows the lure of buying high-end gear. Surely, an expensive guitar and a best-in-class amplifier will hide the fact that you only know a few chords and maybe the lead line to that one song you keep hearing on the radio. What most players find out, however, is that spending thousands of dollars on gear doesn't change the fact that you're not that good yet.

4 foundations you need to scale AI in engineering

As a baseline, engineering leaders need their teams to adopt AI tools to speed up velocity and ship faster. Most organizations have already rolled out AI coding assistants or are evaluating them, but there's a really big difference between buying a tool and successfully scaling it across an engineering organization. If you layer AI on top of a chaotic codebase or a disorganized service catalog, you accelerate the creation of legacy code.

Production readiness review checklist & best practices

Modern software systems are more distributed, complex, and business-critical than they've ever been. A single misconfigured service can take down an entire platform. Teams are aiming for production readiness, which is the state where your services are secure, reliable, observable, and owned. Production Readiness Reviews (PRRs) are one of the key mechanisms to get there.

A buyer's guide to engineering intelligence platforms in 2026

You're in a planning meeting when someone asks a simple question. How long does it actually take your team to ship a feature? You've got spreadsheets, Git logs, and Jira exports scattered across three tabs, and you still can't give a confident answer. It's a question you should be able to answer instantly, but the data lives in too many places to stitch together on the fly.

Navigating the human challenges of IDP adoption

Pragya Jazwal, Platform Engineering Lead at Paxos, compared standing up an internal developer portal to buying a gym membership during her talk at IDPCON 2025. Purchasing the software is one thing, but convincing a team of busy engineers to change their daily habits is a much bigger monster to tame. Pragya says the platform team at Paxos learned this lesson the hard way.

The business case for internal developer portals in 2026

Throughout 2025, we watched AI transform from a novelty into a non-negotiable requirement for engineering teams. Leaders moved quickly to roll out coding assistants, driven by the promise of unprecedented velocity. But as we settle into this new reality, it’s becoming clear that there is a massive difference between buying a tool and successfully scaling it. You can't just drop AI into a complex organization and expect it to work without a solid foundation.