How Separate Scan and Recommendation Agents Power a Memory-Driven GEO Visibility System

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A developer has detailed the architecture of an AI pipeline designed to measure and improve a brand's visibility in generative AI search engines like ChatGPT and Perplexity. The system uses two distinct modules: a Scan Agent that generates customer-style queries, collects structured brand mention data, and logs competitor appearances, and a Recommendation Agent that interprets those results alongside a historical actions log. Keeping the agents separate allows each to be tested independently and ensures scan data remains reusable evidence rather than single-use prompt input. A built-in memory layer called Hindsight tracks past recommendations and their outcomes, enabling the system to refine advice as more scan history accumulates. The pipeline is validated by testing recommendations at different history stages — scans 1, 5, and 10 — to confirm that memory genuinely influences outputs rather than simply being stored unused.

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