A cloud platform is judged on a bad day. You need to roll back fast and hear about the problem before your customers do.
Infrastructure as code
Every resource lives in Terraform and is reviewed like application code. A system you cannot rebuild from source is one you can only repair, never recover.
Deployment
If rollback is slower than fixing forward, teams fix forward at 2am. That is where the second outage comes from. We build pipelines so reverting is the cheap option. On a device platform this cut firmware release cycles 25%.
Observability
We alert on symptoms users feel, rather than causes that happen to be measurable. On a federal-scale policy server, we added proactive telemetry with automated alerting on the conditions that preceded incidents. Global operational support workload fell 60%. The number of problems stayed the same, but far less human attention was needed to notice them.
AI
Our AI work draws on post-graduate study in AI & Machine Learning at Caltech CTME. We start by asking whether a deterministic solution would be cheaper and more reliable. Often it is, and that is a fine outcome.
Where AI does pay, it ships with evaluation, guardrails, a defined failure path and a stated cost and latency profile. An LLM call with no evaluation harness is an unmonitored dependency on someone else's model.