AI Governance Checklist: Your LLM Architecture Comes First

Deloitte reports a 53-point gap between AI ambition and AI governance maturity. 74% of enterprises plan to deploy agentic AI within two years, while only 21% have a mature model for governing autonomous agents. For engineering teams, an AI governance checklist only becomes useful when it maps to architecture, because policy language cannot show who called which model, which provider handled the request, or where the audit trail lives. Three routing postures, managed gateway, self-hosted gateway, and direct API, map differently against the governance requirements your LLM stack can actually satisfy. The governance gap shows up when agentic AI leaves pilot projects and enters production workflows. For engineering leads, that gap becomes concrete when model calls touch customer context, route through external providers, and create audit questions the stack cannot answer. Deloitte also found data privacy and security ranking among leaders’ top concerns about AI. That puts engineering leads in an awkward position. Your team may already route model calls into production workflows while governance still lives…