Governing Coding Agent Sprawl with Databricks AI Gateway

I've watched AI coding agents multiply across our infrastructure like uncontrolled ETL jobs, each team spinning up their own Claude or GPT instances without visibility or guardrails. The governance challenge Databricks highlights here cuts deeper than mere cost control—it's about maintaining data lineage and security as agents become first-class data consumers. When coding agents start querying your lakehouse, they need the same audit trails and permission models we've built for human engineers. The practical implication is shifting from agent experimentation in isolation to a centralized governance layer that monitors what models access, what they generate, and how those artifacts flow downstream. This mirrors our evolution from data silos to unified lakehouse architecture. My recommendation: treat AI gateway governance like you'd approach medallion architecture—bronze (raw agent outputs), silver (validated, lineaged code), gold (approved patterns). Without this framework, you're trading engineering velocity for compliance debt.