Operations | Monitoring | ITSM | DevOps | Cloud

You’ve probably heard of OpenClaw. Maybe you’ve seen the demos where an AI agent opens a browser, navigates to your CRM, fills in a form, and files a support ticket. No API required. Maybe you thought “that’s cool but I’d never run that at work.” Your employees already are. According to Permiso’s research, 22% of enterprise customers have employees running OpenClaw without IT approval.

Traditional synthetic data generation approaches, still called “Test Data Management” (TDM) by legacy vendor, were designed for a world where applications were monolithic, databases were the center of gravity and change happened slowly. The world looks a lot different now. Modern systems are distributed, often times event-driven, and increasingly powered by streaming data and AI agents. In this environment, batch-oriented synthetic data generation fails to capture how systems actually behave.

In Part 1 and Part 2 we explored why testing modern software is so difficult. Production data is the most valuable input for testing, but it’s locked away because it contains PII and sensitive context. Traditional Synthetic Data Generation (SDG) was built for batch databases, not streaming systems. And AI coding agents amplify every weakness in existing test strategies because they need current, realistic data or they generate buggy code based on outdated assumptions.

According to our new report, FinOps in the AI Era: A Critical Recalibration, 40% of companies now spend $10M or more annually on AI. Most can’t tell you if it’s working. That’s not a budgeting problem. It’s a systems problem. And Donella Meadows wrote the playbook for understanding it.

I've been in this industry since before the word "PaaS" existed. I founded Cloud 66 in 2012 — the same year Heroku was peaking, dotCloud was pivoting to become Docker, and the idea of "just git push and forget about servers" felt like the future. It was the future. Partly. The deployment experience was revolutionary. The business model wasn't. Last week, Heroku announced its transition to "sustaining mode" — no new features, no new enterprise contracts.

Last week, Heroku announced it is transitioning to a "sustaining engineering model." In plain English: no new features, no new enterprise contracts for new customers, and Salesforce is redirecting its investment elsewhere. The platform will be maintained for security and stability, but that's it. If you've been in this industry long enough, you know what "sustaining mode" means.

We've all heard from an engineering leader about the exact moment they realized their architecture had gotten too complex. It usually happens when they look at a service map and realize it looks like a box of tangled Christmas lights. This cognitive overload is exactly what Steve Evans, the former SVP of engineering at Chegg, reflected on in a recent post on LinkedIn. He argued that microservices were a trillion dollar mistake because we often over-build for future problems that never actually arrive.

The decision to migrate a production application is rarely about the destination. It is about the friction of the journey. For most engineering leaders, the word "migration" is a synonym for "refactor." The industry has conditioned us to assume that moving to a modern cloud platform requires throwing away years of stable configuration, learning a new proprietary DSL, and rewriting core application logic to fit a specific container or serverless model.

In a recent technical evaluation by Journal du Net (JDN), Upsun (formerly Platform.sh) was recognized for its ability to "pull ahead" (tire son épingle du jeu) in a fiercely competitive market dominated by cloud giants and specialized pure players. While hyperscalers offer raw power, Upsun’s strategic fusion of enterprise reliability and AI-ready agility has redefined expectations for modern PaaS.

Most teams choose standard on-call patterns like weekly or daily rotations. But sometimes a less conventional rotation can solve a specific problem or just fit better with how your team works. This guide walks you through five offbeat on-call rotations. For each, we look at why it might work for you and the challenges involved. This helps you see the full picture before you decide to try them out. Let’s dive in!