Agent Analytics

Agent Analytics refers to measurement for agentic behavior rather than single-turn answers alone. As AI products move from answering a question to researching, comparing, and sometimes taking action, brands need to understand how these systems encounter and use their content through longer workflows.

This is different from basic mention tracking. Agentic systems may fan out across multiple queries, revisit the same domain, compare sources, or move from research to transaction without surfacing each intermediate step to the user. Agent Analytics tries to capture that wider path: which sites were considered, which pages were reused, which claims were trusted, and where the brand dropped out of the flow.

In practice, Agent Analytics often combines source tracking, query-path analysis, and action-stage visibility. It helps teams understand whether they are only being cited in educational steps or whether their commercial and proof pages survive into shortlist, evaluation, or recommendation stages.

For landing-page strategy, the implication is clear: category copy alone is not enough. Agentic discovery also depends on pricing, comparison, methodology, security, and supporting definitions being available when the agent needs them.