As AI-assisted engineering matures, platform teams need the same controls that make cloud platforms reliable: cost visibility, ownership, sensible defaults, observability, governance, and evaluation loops. Token usage should be treated like CPU or memory — a consumption signal requiring context, not a value proxy. Azure API Management's GenAI gateway capabilities (token rate limits, quota policies, telemetry) provide a shared control point for model consumption. GitHub Copilot usage metrics and team-level reporting help attribute spend. Workload-owned AI resources should be deployed via approved Terraform modules with networking, diagnostics, and tagging baked in. MCP servers and agent skills need explicit ownership and permission models. Governance should work through paved roads — repeatable workflows with evaluation loops — rather than policy documents that teams route around.
