Foundations
Earned Agent Citation explained
Earned Agent Citation, or EAC, is the evidence-backed moment when an assistant uses your business in the answer.
EAC is not the same as ranking for a keyword. It is an answer outcome. The assistant names, cites, or recommends a business because the public evidence is strong enough to support that choice.
AgentFound uses EAC as the central score because AI discovery is compressed. A user often sees a synthesized answer, a small shortlist, a map card, or a direct action. If the business is not selected into that answer layer, it may be technically online but practically invisible.
What EAC rewards
EAC rewards observable confidence, not hidden weights. A stronger score usually means the business has:
- Clear entity facts across the website and profiles.
- Crawlable pages with visible, answerable content.
- Valid structured data that matches the page.
- Credible third-party corroboration.
- Current hours, services, and action links.
- Assistant answers that name, cite, or recommend the business more often.
What EAC does not reveal
The public standard documents the signal families and verification logic. It does not publish the full scoring recipe or signal weights. That protects the product from becoming a checklist for manipulation and keeps the focus on improving real assistant outcomes.
Scoring principle
Signals are weighted by observed impact on assistant outcomes. The docs explain what matters and how to fix it, but not the proprietary weighting model.
How EAC changes ownership
EAC turns vague "AI visibility" into a practical workflow:
- Run the audit.
- Identify the weak signal.
- Apply the matching fix playbook.
- Re-run the audit.
- Confirm that the assistant answer changed.
That last step matters. A page update is not the same as an answer update. A check turns green only when the assistant can now see and use the improved evidence.
Measure the outcome
Treat EAC as a field test of what assistants can currently verify, not as a static technical grade.
Run scan