What is agent-to-agent (A2A) attribution?

Why the linear model breaks

Classical attribution assumes a user who sees an ad, clicks it, arrives somewhere, and converts. Every model built on top of that — last click, first click, multi-touch attribution — is a way of dividing credit along a path the user personally walked.

Agentic systems break the assumption at the first step. A user asks an assistant to find them a supplier. The assistant calls a research agent, which calls a comparison tool, which surfaces three candidates, one of which is a brand that paid for inclusion. The user approves; a booking agent completes the purchase. Nobody clicked an ad, and the user personally traversed none of the path.

What A2A attribution replaces it with

The path becomes a graph. Each hop is an agent call with an input, an output and a caller, and the question is which hops materially changed the outcome.

That reframing has two useful properties. It is honest about influence being distributed — several agents genuinely did contribute. And it is auditable in a way click paths never were, because each hop is a logged call with structured arguments rather than an inferred touchpoint reconstructed from cookies.

• Hops are recorded, not inferred. An agent call is an explicit event with a known caller and callee.
• Credit is assigned per hop. A hop that introduced a brand into consideration is weighted differently from one that merely passed a result along.
• The chain terminates in an outcome, which may be an agentic conversion rather than a site visit.

What it does not solve

A2A attribution tells you which agents were involved. It does not tell you whether the outcome would have happened anyway, which is the question every attribution model has always struggled with and the reason incrementality testing exists.

It also depends on the chain being observable. Where an agent is a closed system that reports only its final answer, the intermediate hops are invisible and the graph collapses back to a single opaque step. This is a practical limit today rather than a theoretical one, and it is the same visibility problem measuring AI assistant performance honestly runs into.

The honest position for a buyer in 2026 is to treat A2A signals as directional evidence of influence, and to keep a holdout for the question of whether the spend caused anything.