Operations | Monitoring | ITSM | DevOps | Cloud

In the AI era, software and new models are being born at a breakneck pace—but they’re also bringing a lot of “baggage” into the world. While AI coding agents are busy accelerating innovation, they’re also excellent at generating a massive byproduct: “digital dust.” Between obsolete releases, orphaned dependencies, and massive model versions, your repository may soon start to look more like a digital junk drawer than a streamlined machine.

We are witnessing a fundamental transformation in how software is built. The industry has moved beyond the experimental phase of Machine Learning Operations and entered a complex new reality: the era of the AI Software Supply Chain. The adoption metrics confirm this shift is irreversible. Google reports that 90% of tech workers are now using AI as part of their daily work. Similarly, McKinsey data reveals that 88% of organizations use AI in at least one business function.

In my previous posts, we discussed the where and the how of managing your ML assets. We showed you how JFrog Artifactory acts as a powerful, universal model registry (the “where”) and how the FrogML SDK serves as the gateway to get your models and metadata into it (the “how”). Now, let’s talk about the why.

Amazon Elastic Kubernetes Service (Amazon EKS) is a fully managed, compliant Kubernetes service that simplifies running, managing, and scaling containerized applications. EKS automatically handles the availability and scalability of the Kubernetes control plane, allowing teams of any size or skill level to focus on building and deploying production-ready applications across diverse environments, including AWS, on-premises, and at the edge.

When we first introduced the JFrog AI Catalog, it was our mission to provide the industry with a single system of record for governing the complex landscape of internal, open-source, and external commercial AI models. This foundational step was critical for enterprises to move from uncontrolled innovation to delivering AI with trust and confidence. However, the AI landscape is ever-evolving. The challenge for today’s enterprise is already evolving beyond simply managing a library of known models.

A fundamental shift in software development is already here. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants – a massive leap from less than 10% in early 2023. While this AI-driven speed creates a competitive advantage, it also opens a dangerous new front in the battle for software supply chain security.

AI has created a paradigm shift in software development. AI-native development teams – from small startups to enterprises like Goldman Sachs and Google – are adopting agentic development tools like Cursor and Copilot to increase the speed of code generation to a pace we’ve never seen before. But with all this new code comes a big challenge: how do you manage all these potential new releases and get the right ones deployed?