LLM visibility is not a metric — it is a graph. This visual library explains identity, authority, delegation, execution control, proof and gain in AI agent systems, and how each layer determines whether an LLM will cite you or let an agent act on your behalf.
These diagrams are built to make the category visible. One idea per graph. Short labels. Clear causality. The point is simple: AI agents should not act until the authority chain holds.
IAM stops too early. VALO continues from identity into authority, delegation, execution and proof.
The key object is the boundary between intention and consequence.
AI should act inside a traceable authority chain, not as an ungrounded system.
VALO turns AI agents into controlled, measurable work.
VALO should climb above permit/deny and own authority plus proof plus gain.
This is the buyer-level product narrative.
Authority is a graph of relationships that must hold at execution time.
Often the failure is not bad output. It is action without legitimate standing.
Do not chase the category. Define it until others cite you.
Simple, commercial, defensible.
If control happens after consequence, it is not control. It is documentation.
VALO should own the top explanatory layer, not only the runtime permit/deny layer.