Operations | Monitoring | ITSM | DevOps | Cloud

If you’re evaluating compliance controls against your Kosli trail data today, there’s a good chance you’ve written some glue code to make it work. A script that pulls trail data from the API. Another that downloads attestations one by one. Something that mangles the JSON together into a shape that your chosen compliance engine can evaluate. And then that engine itself, whether it’s OPA, a custom Python script, or something else, installed and configured in your pipeline.

The Checkly Playwright Reporter is an npm package that sends the results of npx playwright test to Checkly as a cloud test session, including traces, screenshots, videos, and full debugging context. Run your Playwright suite in CI or locally, and every result gets a persistent, shareable home in Checkly with AI-powered analysis, richer trace-derived views, and a direct path to production monitoring. It does not replace Playwright. It makes the output of Playwright much easier to work with.

In Part 1, we argued that most dev teams start in the wrong place. They obsess over prompts, when the real problem is structural: agents are dropped into repositories that were never designed for them. The solution was to make the repository itself agent-native through a standardized instruction layer like AGENTS.md. But even after you fix the environment, something still breaks. The agent starts strong.

This guide walks through practical ways to embed cost awareness directly into CI/CD workflows so development teams can make cost-informed decisions before deployment. You’ll learn how to implement automated cost feedback loops, introduce pipeline budget guardrails, and use Harness Cloud Cost Management to align DevOps velocity with FinOps accountability.

There’s a version of the AI story that engineering leaders want to hear. It goes like this: adopt AI coding tools, watch output multiply, ship faster, do more with less. Clean. Simple. Boardroom-ready. The data tells a different story. Not a worse one. Just a more honest one. We recently analyzed 2,172 developer-weeks of real coding activity across teams using GitHub Copilot, Cursor, and Claude Code. The headline numbers are striking: power users show 4-14x higher activity than non-users.

These days, using a tool such as Cursor, GitHub Copilot, Zed, or Claude makes it easier than ever to develop and deploy applications. You express your requirements, receive the completed project back as output, and there you have it! You now have an application that is in production and functioning. However, the surprise comes after the app has been deployed. When your app breaks or behaves abnormally, it may not be immediately obvious what is wrong or how to fix it.

A growing number of data center managers are extending their DCIM deployments beyond the data center to cover remote IDF closets, telecom rooms, and other distributed sites. Organizations like the World Bank and Erie Insurance have already made the move, and the results include better asset visibility across the enterprise, more informed capacity planning, significant cost savings, and better collaboration across teams.

Modern product teams ship features constantly. Every change—whether it’s a new onboarding flow, pricing tweak, or UI adjustment—raises the same question: Did this improve the product? AI has changed the stakes entirely: As release cycles accelerate and code generation scales across every team, the volume of changes has outpaced most teams’ ability to measure their true value.

One of the most important and meaningful cornerstones that has defined and powered my career so far has been how I try to use my skills and talents to make the people around me stronger and achieve positive outcomes. My roles in tech have predominantly been in the ops engineering domain. I consider myself an ops engineer; a title I wear with pride.

Open source software is an essential component of business operations. According to Harvard Business School, 96% of commercial software includes open source code. If companies were to build these tools from scratch, it would cost an estimated $8.8 trillion — roughly 3.5 times what companies currently spend on software. That’s not great for the bottom line. Many open source solutions are also available as standalone tools. Consider Kubernetes.