Operations | Monitoring | ITSM | DevOps | Cloud

Back in May of 2021, containers had already won. Kubernetes adoption was surging. ECS and EKS were powerful. But for many teams, deploying a simple containerized web service still meant stitching together clusters, networking rules, scaling policies, load balancers, IAM roles, and CI/CD pipelines. It felt heavier than it should. Developers no longer wanted more orchestration power. They wanted less operational drag.

This is part 1 of a five-part series on building production-grade AI engineering systems. Across this series, we will cover: Most teams experimenting with AI coding agents focus on prompts. That is the wrong starting point. Before you optimize how an agent thinks, you must standardize what it sees. AI agents do not primarily fail because of reasoning limits. They fail because of environmental ambiguity.

A FinOps analyst walks into a Monday morning meeting with a detailed spreadsheet showing $2.3M in potential Kubernetes cost savings. The recommendations look straightforward: reduce memory limits by 40%, scale down replicas during off-peak hours, consolidate workloads onto fewer nodes. The numbers are compelling, the methodology is sound, and the savings would make a material impact on quarterly cloud spend. The SRE team immediately objects.

We are thrilled to announce a set of product updates across our dotConnect data providers and Entity Developer. The new release adds support for Entity Framework Core 10, introduces AI-focused vector data types across major databases, and extends compatibility with the newest platform capabilities such as SQL Server 2025, Oracle 26ai, and Microsoft Entra authentication.

Recent outages tied to AI-assisted code changes have pushed companies into a corner. After several incidents with massive “blast radius” impacts, organizations like Amazon introduced stricter controls—mandating that senior engineers manually review all AI-generated code before it hits production. That response makes sense on paper, but it exposes a fatal flaw in the modern development pipeline.

While the race to expand networking infrastructure to support AI applications continues on land, the same expansion is occurring undersea. Subsea cables spanning Earth’s oceans must evolve to keep pace.