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Cortex Wrapped 2025: The Year of AI Excellence

Every December, Spotify launches its infamous Wrapped campaign, which sends millions of users into a frenzy about their listening habits. They become pseudo data scientists and analyze how frequently they listen to their guilty pleasures, their kids' terrible playlists, or the music they love that nobody else has heard of yet. We love this tradition, so we're bringing it to Cortex.

Cortex and Rootly partner to help teams turn incidents into continuous improvement

For many engineering teams, an incident is a chaotic, all-hands-on-deck event. Once the incident is resolved, everyone returns to their regular work and the valuable lessons from the incident are often lost. The result is a cycle of repeated failures and engineer burnout, where incidents are something to be survived, not learned from. At Cortex, our mission is to help engineering organizations build a culture of continuous improvement.
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End-to-End Testing for Microservices: A 2025 Guide

End-to-end testing has always been a double-edged sword - even more so in the world of microservices. On one hand, E2E tests are critical for validating that all services work together seamlessly in real user flows. On the other hand, many experts warn that heavy reliance on end-to-end testing in a microservices architecture can create a "distributed monolith," slowing down deployments and undermining the very agility microservices promise. There's truth to that: if done poorly, E2E tests can become brittle, flaky, and a bottleneck that reduces your deployment frequency.

10 platform engineering tools your devs will thank you for

Modern engineering teams are shipping more services, managing more complex infrastructure, and moving faster than ever. But this velocity often comes at a cost to the developer experience. Engineers are frequently bogged down by infrastructure complexity, inconsistent tooling, and a lack of clear standards, which leads to cognitive overload and slower cycle times.

The engineering leader's guide to AI tools for developers in 2026

The holiday shopping season is a familiar ritual for many. We spend hours researching the best deals, comparing features, and reading reviews to make sure we’re investing in the right things. As we all come to grips with the fact that 2026 is right around the corner, engineering leaders are doing the same thing, but largely in response to the explosion of AI developer tools.

The most important question to ask in the build vs. buy debate

Every growing engineering organization eventually faces the seemingly impossible decision between building a custom solution or buying one off the shelf. It’s a debate that often (and incorrectly) ends by choosing whichever option is less expensive. However, it’s become clear that solving the build vs. buy puzzle boils down to understanding what you want to be good at and whether your internal build is actually unique.

Get more from your AI chief of staff with these prompts for engineering leaders

Engineering leaders face a constant barrage of questions that pull them away from strategic work. A team lead asks about scorecard compliance. A PM wants a status update on a migration. Someone needs incident trend data for a quarterly review. Each question is reasonable. Each requires context switching, digging through dashboards, or pinging someone on your team for a report. What if you could just ask?

Modern Service Architecture for High-Velocity Operations

Modern service architecture supports organizations that target sustained velocity, predictable delivery cycles, and scalable global operations. Cloud-native platforms, microservices patterns, and distributed execution models now anchor these environments. Modern service architecture emphasizes modularity and flexibility, which contrasts with traditional monolithic approaches. The 2025 Gartner Magic Quadrant for Cloud-Native Application Platforms identifies AWS, Red Hat OpenShift, and Heroku as leaders because they strengthen developer experience, platform engineering, and security.

Distributed Tracing for Microservices: 10 Essential Best Practices for 2026

Distributed tracing tracks how a single request moves across multiple microservices, helping teams see the entire execution path end to end. In modern architectures where dozens of services interact, it becomes difficult to understand where latency starts, why bottlenecks appear, and which component breaks under load. Traditional monitoring only shows isolated metrics. Distributed tracing connects those dots.

Our Engineering in the Age of AI: 2026 Benchmark Report finds AI is making engineering faster, but not necessarily better

Everyone's talking about how AI is transforming software development. Teams are shipping more code, deploying more frequently, and getting features to market faster than they could a year ago. The productivity gains are real. But we kept hearing a different story from engineering leaders. Yes, velocity is up. But incidents are climbing, resolution times are getting longer, and code review processes are struggling to keep up.