#human engineering team

Autonomous AI engineer agents can deliver software at a scale in multiples of what a human engineering team can do, and that productivity is genuinely valuable. But without proper guardrails at the specification level, these agents can industrialise inefficient infrastructure patterns at the same pace, consistently and at a scale that makes post-deploy remediation impractical. When an agent provisions a three-node GKE cluster using n2-standard-16 machines for a workload a single e2-medium node could handle, or generates a Kubernetes pod spec with 4-CPU and 8GB memory requests for a service that peaks at 200 milli-cores and 256MB, or writes a Dockerfile that pulls a full Ubuntu base image where a distro-less container would serve, infrastructure runs that decision continuously, for the lifetime of the service. The agent will reproduce these patterns across every environment it touches, because the specification never instructed it otherwise. When agentic pipelines are generating infrast