Operations | Monitoring | ITSM | DevOps | Cloud

The way software gets built is changing faster than at any point in the last decade, and the software supply chain has to keep up. A year ago at swampUp, we introduced JFrog Fly: the first agentic repository, serving as a fast, AI-native environment to build what a modern, agentic developer workflow could look like, unconstrained by the shape of the enterprise stack. Today, we’re bringing what we learned back home.

This one is for the curious souls who wonder how somebody actually builds a harness optimizer — and, more to the point, how they know it works. When we launched JFrog Boost into public preview, we told the story of the bill that broke us and the 100 billion tokens we clawed back across JFrog R&D. What that post didn’t cover is the question that consumed most of our engineering time: how do you measure any of this?

Teams running containerized workloads on Google Cloud have long relied on JFrog as their single source of truth for container images. The missing piece has been getting Google Cloud’s own runtime services — like Cloud Run and Google Kubernetes Engine (GKE) — to pull directly from JFrog for every container image pull. I’m happy to say that the gap is now closed. Artifact Registry in Google Cloud has introduced a new repository mode called Connector that addresses this requirement.

Software supply chain engineering has reached a critical inflection point. As autonomous AI coding agents transition from generating autocomplete suggestions to planning, writing, reviewing, and deploying entire software pipelines without humans in the loop, the connection between human intent and production binaries is fracturing.