Operations | Monitoring | ITSM | DevOps | Cloud

Our December Harness AI updates focus specifically on enhancements to Harness AI governance. You can review our Harness AI November updates here.

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.

Technical debt refers to the future costs and limitations incurred when organizations opt for short-term solutions over robust, long-term scalable architectures. For the middle mile, technical debt often manifests as equipment or network designs that restrict long-term flexibility, scalability, or interoperability.

In 2025, DevOps teams faced a pivotal moment. The era of treating security as an afterthought was over. Practically overnight, airtight protection became a non-negotiable requirement across every layer of the technology stack, whether on prem, in the cloud, or at the network’s edge. For many teams, this wasn’t just a technical hurdle; it was a daily source of stress.

Think about how airlines assign seats on a plane. Some have extra legroom. Some sit near exits. Some are cheaper, while others cost a premium. Certain passengers also have strict requirements, like families traveling together or travelers who paid for a specific class. Now imagine boarding everyone randomly. A passenger who paid for extra legroom (perhaps for health reasons) ends up squeezed into a middle seat. Families scatter across the cabin. Premium seats sit half empty while the back rows overflow.

Manual mTLS vs. Service Mesh: Which is right for your Kubernetes cluster? Explore the operational risks of manual setups and the benefits of node-based encryption.