Great comprehensive stack! I'd add one critical missing layer: AI Governance Utilities.
While you have Model Safety (#9) and Model Supervision (#8), there's no standardized authorization and access control layer for AI-to-AI interactions, agent-to-service access, and multi-agent coordination.
KROG THEOREM could serve as a horizontal governance utility across all 9 levels - similar to how TCP/IP standardized network communication or OAuth standardized authentication. It provides mathematical frameworks for:
Agent-to-Agent authorization (A2A governance)
Agent-to-MCP server permissions (A2M governance)
System-to-system AI integration (M2M governance)
This addresses the #1 enterprise AI adoption blocker: "How do we control what our AI agents can do and prove compliance?"
Just as every level needs monitoring and safety, every level needs governance. The stack is technically complete but missing the authorization infrastructure that enterprises require for production deployment.
Think: "The TCP/IP of AI governance" - foundational infrastructure that enables safe AI interactions at scale.
What do you think? Is AI governance infrastructure mature enough to be considered essential stack infrastructure, or still too early?
Great article. This is really useful for those starting out and learning in this space!
Great tech stack! Would love to see an article on Fintech next!
Great comprehensive stack! I'd add one critical missing layer: AI Governance Utilities.
While you have Model Safety (#9) and Model Supervision (#8), there's no standardized authorization and access control layer for AI-to-AI interactions, agent-to-service access, and multi-agent coordination.
KROG THEOREM could serve as a horizontal governance utility across all 9 levels - similar to how TCP/IP standardized network communication or OAuth standardized authentication. It provides mathematical frameworks for:
Agent-to-Agent authorization (A2A governance)
Agent-to-MCP server permissions (A2M governance)
System-to-system AI integration (M2M governance)
This addresses the #1 enterprise AI adoption blocker: "How do we control what our AI agents can do and prove compliance?"
Just as every level needs monitoring and safety, every level needs governance. The stack is technically complete but missing the authorization infrastructure that enterprises require for production deployment.
Think: "The TCP/IP of AI governance" - foundational infrastructure that enables safe AI interactions at scale.
What do you think? Is AI governance infrastructure mature enough to be considered essential stack infrastructure, or still too early?
Great resource