AI agent governance at scale: from 5 agents to a 500-agent workforce

What this post added

This post extends the 'Agentic AI Governance' feature thread by detailing the infrastructure required to scale AI agent governance from a few agents to hundreds. It elaborates on the challenges of manual governance breaking down at scale, the necessity of centralized controls, and the specific components of governance infrastructure: agent registry, identity, policy propagation, permission scope, tool access, component lineage, runtime enforcement, monitoring, audit trails, and review triggers. It specifically unpacks the importance of centralized agent identity, policy propagation, and cross-environment enforcement as critical differentiators for large-scale governance.