Every new service environment was a four-hour ticket: someone hand-clicked Cloud Run, Cloud SQL, a bucket and six IAM bindings, and got it subtly wrong about a third of the time. I modularised the GCP estate around one opinionated golden path per service shape, put the guardrails in the module instead of in a wiki, and gated every plan behind automated policy checks. A team now provisions its own environment in under 45 minutes without opening a ticket, and the drift that used to surface in production surfaces in terraform plan.

Known failure classes were eating on-call time not because the fix was hard, but because assembling the context (logs, Terraform state, recent deploys) took twenty minutes at 3 a.m. I built an MCP server that does the gathering and proposes a remediation, and then stops. A human approves or rejects every state-changing action, each one is logged with the reasoning that produced it, and each one is reversible. The agent is allowed to be wrong; it is not allowed to be wrong unsupervised.

80+ trainees each needed a production-like n8n environment, and a fixed t3.medium per person was both wasteful and, at cohort scale, expensive. The workloads are bursty by nature: idle for hours, then a spike. So I put them on burstable instances sized for the median rather than the peak, and wrote lifecycle automation that stops and reclaims anything idle past a threshold. Everyone gets a real isolated environment; compute costs about half of the fixed-size equivalent.