Context
AgenticGEO formulates generative engine optimization as a content-conditioned control problem and uses a MAP-Elites strategy archive plus a co-evolving surrogate critic to adapt optimization strategies to black-box generative engines.
What changed
The research moves beyond fixed GEO heuristics and single-prompt optimization toward an adaptive agentic framework that evolves diverse strategies and uses a surrogate critic to reduce direct engine-feedback costs.
Why it matters
For AI visibility research, this makes optimization strategy itself a dynamic experimental variable and highlights the need to measure transferability across content, domains and changing engine behavior.
Visibility OS interpretation
Visibility OS interprets AgenticGEO as evidence that GEO can be studied as an adaptive intervention process rather than a permanent checklist. It does not establish production ranking factors or deployment by any named commercial platform.
What remains uncertain
How well do adaptive GEO strategies transfer to live production search systems over time? How quickly do gains decay as engines change? Which strategy components affect retrieval, citation, attribution or answer inclusion separately?