Operations | Monitoring | ITSM | DevOps | Cloud

Summary: An API defines how software interacts with a service. MCP defines a standard way for AI applications to discover and invoke tools exposed by a service. They usually work together: an MCP server can sit in front of APIs you already run, turning low-level operations into capabilities an agent can find and use at runtime. Your API may already expose everything an AI agent needs. The harder problem is helping the agent figure out which operations matter for the task it has been given.

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.

Every team adopting coding agents hits the same question early: where do you put the instructions that tell the agent how your codebase actually works? Two answers dominate the conversation right now. One is AGENTS.md, a plain markdown file at the root of your repo. The other is skills, packaged instruction sets an agent loads on demand. Most of the debate treats this as a formatting decision. It isn’t.

“We don’t need junior engineers anymore” has become one of those lines people repeat because it sounds obvious. The AI writes the code, so why pay someone to learn how to write it? On the latest Confident Commit podcast, Rob Zuber makes the case that this take is exactly backwards.

You shouldn’t have to hunt through a cluttered dashboard to understand where your code is deployed. The Deployments page is where teams turn for a quick answer: what is in test, what is in staging, what reached production, and what needs attention right now. The older page made that harder than it needed to be. Our new Pipelines Deployments page makes it easier to scan, filter, and act on.