Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

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.

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.

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 UK’s Competition and Markets Authority (CMA) has now set out its latest actions under the Digital Markets Competition Regime (DMCR), following its multi-year Cloud Services Market Investigation. While the regulator has now expanded its focus into business software ecosystems, we must not lose sight of the core issue: the entrenched dominance within the UK's cloud infrastructure.

The first 70% of a debugging cycle is usually spent on "plumbing", the undocumented toil of syncing databases, matching service versions, and aligning networking to mimic a production failure. This manual setup is a hidden factory that consumes senior engineering capacity and delays recovery. True velocity is found by eliminating the infrastructure variables that make bugs hard to reproduce.

In Star Trek, the Borg are a cybernetic alien organism that forcibly assimilates other beings and technologies into its hivemind called “The Collective.” Each assimilated being or technology becomes part of the unified consciousness, with the villainous Borg Queen as the leaders. As the only independent thinker, the Borg Queen leads this rapidly adapting Collective.

When you’re managing a handful of nodes, the flat list in the nodes tab works fine. When you’re managing hundreds or thousands, it becomes a wall of hostnames. You end up applying the same filters repeatedly: all the production database servers, all the nodes in eu-west, all the Kubernetes workers in the staging cluster. The filters work, but they don’t persist, and there’s no way to share them with the rest of your team. Node groups solve this.