Agent attribution
The practice of measuring which AI agent surface drove a given shopper session or transaction. Made difficult by referrer-stripping; solved by server-side attribution layers.
Agent attribution is the practice of measuring which agent surface drove a given session or order. Because AI surfaces strip the HTTP Referer header on outbound clicks, standard client-side attribution (GA4 default channels, Adobe Analytics) mis-classifies most agent-driven traffic as "Direct."
Working agent attribution requires the server-side attribution layer described in the DACT and Server-Side AI Attribution glossary entries. The three-signal architecture (fingerprinting + query-param negotiation + tracking pixel) produces confidence-weighted per-surface attribution that ties out to revenue.
Agent attribution is prerequisite infrastructure for any brand that wants to make budget-allocation decisions across the six agent surfaces. Without it, brands are literally guessing which surface deserves incremental investment.
See also
DACT (Dark Agentic Commerce Traffic)
AI-driven shopper traffic that GA4 and legacy analytics cannot identify, silently bucketing as 'Direct.' Typical brand gap: 4-8× between measured and actual AI revenue.
Server-side AI attribution
Server-side infrastructure that identifies AI-driven traffic without relying on the HTTP Referer header. Three-signal architecture: fingerprinting, query-param negotiation, tracking pixel.
Citation Rank
Tru Commerce's flagship AI Share-of-Voice score - measures how often and where a brand appears when AI agents answer category-relevant shopper questions.