Operations | Monitoring | ITSM | DevOps | Cloud

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.

Apache ActiveMQ is evolving from simple transport to intelligent fabric. Key shifts include replicated KahaDB for cloud-native resilience, Spring decoupling in v7, and OpenTelemetry observability—transforming messaging infrastructure for modern enterprise needs.

Phoenix LiveView is one of those technologies that feels like cheating. You get rich, interactive UIs without writing JavaScript, and the server handles the state. It’s elegant. But that elegance comes with a trade-off that’s easy to forget: all that interactivity runs on your server.

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.