RAG systems fail in production for predictable reasons: retrieval quality degrades silently, embedding drift goes undetected, LLM latency spikes under load, and observability is bolted on after incidents. This blueprint addresses all four with a complete operational architecture.

Most engineering teams operate in reactive mode — waiting for alerts, scrambling to diagnose incidents, and applying fixes under pressure. AIOps changes this fundamentally by applying machine learning to operational data, enabling teams to predict issues before they impact users.

Running Kubernetes in development is easy. Running it in production is where teams get burned. After helping dozens of teams move to production Kubernetes, here's the checklist we use in every engagement.

Every team starts with a single main.tf. Six months later, it's 2,000 lines of spaghetti that nobody wants to touch. Here's how we structure Terraform at scale in our consulting engagements.

Observability isn't about collecting more data — it's about asking better questions. In 2026, the observability landscape has matured significantly, and there's a clear winning stack for most teams. Here's how to build it.

Platform engineering has moved from a buzzword to a critical function. The premise is simple: instead of every team solving the same infrastructure problems independently, build a platform that gives developers self-service access to production-ready infrastructure.