Mobility traces are known to be highly unique, but re-identifying people from them has historically required substantial manual effort by skilled analysts. This feasibility study shows that agentic AI changes that threat model: an end-to-end pipeline of large-language-model agents autonomously searches the open web, cross-references public records and social media, and resolves raw coordinate sequences to candidate identities without human intervention. Evaluated on spatio-temporal data anchored around home and work addresses, the agents re-identified a large share of the re-identifiable individuals at a cost of minutes and dollars per target. The paper discusses what this means for statistical disclosure control and for the GDPR’s “reasonably likely” standard of identifiability.