Operations | Monitoring | ITSM | DevOps | Cloud

AI coding assistants like Cursor and Claude Code are driving massive productivity gains, yet they have introduced a critical validation gap in the software delivery lifecycle. While these tools excel at generating syntax, they lack visibility into live production environments. This article explains how Runtime Context, the missing nervous system of AI development, secures production by moving from probabilistic guessing to deterministic, live code validation.

How we built an AI agent that implements Jira tickets, creates merge requests, and tracks them on its own, and our journey to get to that point.

We’re excited to announce that Speedscale’s proxymock is now available as an OpenClaw skill on ClawHub, bringing powerful API traffic replay capabilities directly into your Claude workflows.

AI has made it easier than ever to write code. Shipping it is a different story. Today we released the 2026 State of Software Delivery report, sponsored by Thoughtworks. In it, we analyzed more than 28 million CI/CD workflows across thousands of engineering teams. The picture that emerged is clear: teams are producing more code than ever, but fewer of them are able to turn that activity into software that actually reaches customers.

AI coding agents are excellent at writing code. Ask Claude Code, Codex, or Cursor to add a feature, and it just works. At Checkly, we were ready for the new agentic world from the start! Monitoring as Code means your entire monitoring setup lives in your repository. API Checks, Browser Checks, alert channels, status pages; everything is defined in code, managed with the Checkly CLI, and version-controlled like any other part of your stack.