Operations | Monitoring | ITSM | DevOps | Cloud

As AI agents become ubiquitous across the software development lifecycle, engineering teams must do more than adopt new tools; they must redesign how they build, verify, and operate software. This post distills the vision, priorities, and best practices that guide engineering excellence at Harness. Different products sit at the heart of the Harness platform.

Modern engineering teams have become exceptionally good at shipping software quickly. With modern CI/CD platforms, what once required careful coordination, late-night release windows, and layers of approvals now happens almost invisibly. Pipelines execute in minutes. Releases flow continuously. The friction that once slowed everything down has been engineered away. From the outside, it looks like progress in its purest form. Automation removed bottlenecks. Cloud infrastructure removed limits.

If you're building a modern web application in 2026, Vercel is almost certainly on your shortlist, and probably near the top of it. The developer experience Vercel pioneered for Next.js and the frontend ecosystem around it is a real achievement. Push a branch, get a preview URL, ship. It works, it's fast, and an entire generation of frontend teams have built their workflow around it. This article is not here to argue with any of that.

Scaling your deployments to zero is only half the battle. If your cluster autoscaler does not aggressively bin-pack and terminate the underlying worker nodes, you are still paying for idle metal. True environment sleeping requires tight integration between your ingress layer and your node provisioner to actually realize FinOps savings.

UK organizations are increasingly required to design systems that account for data residency requirements, ensuring that operational data remains within national boundaries. Many teams already run their applications on AWS infrastructure in the UK, but telemetry data can still be processed outside the region, creating gaps in visibility. Datadog’s upcoming UK availability zone solves this by keeping telemetry data in the same region as the workloads that generate it.

Developers and SREs who rely on Microsoft Azure DevOps often face fragmented workflows when investigating issues or reviewing code quality. Troubleshooting an error can require jumping between observability tools and source code repositories as you manually connect traces, stack frames, and commits. At the same time, security vulnerabilities, misconfigurations, and flaky tests may go undetected until later stages of the software delivery life cycle (SDLC), where they are more costly to fix.

Claude Opus 4.7 holds at $5/$25 per million tokens — but a new tokenizer inflates costs up to 35% on identical text. Here's what Opus 4.7 actually costs at production scale, how it compares to Sonnet 4.6, and the six levers that determine where your bill lands.

Make AI do the dirty work while you focus your energy on strategy. CloudZero's Ryland Bowles shows you how. Every FinOps engineer is worried that AI is going to steal their job. I’ve worried about it. But I’ve also experimented extensively with AI, and I’ve got a pretty clear sense of what it can and can’t do in a FinOps context.

AI Classify reaches general availability this month, TDM gets a Docker image, and we’ve added a few things you’ve been asking for.

For years, infrastructure stability could be approximated through static limits. If CPU utilization exceeded a defined percentage or response time crossed a fixed boundary, risk was assumed to increase in a predictable way. Monitoring systems were designed around that assumption, and for contained environments, it largely held true.