Building AI Agents That Actually Work for Enterprise SEO

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  Part 1Building AI Agents That Actually Work for Enterprise SEO

  Agentic AI workflows fail without classified first-party data. Venkata Pagadala, AI Product Manager for Search at AT&T, classified 5.8 million queries, removed 3 million noise queries, and uses the resulting persona-based insights to build mockups that win leadership buy-in. He outlines a competitor-navigation and Wikipedia-driven L1/L2/L3 taxonomy that surfaces zero-search-volume, high-intent demand from Reddit, Quora, and YouTube, plus a UGC-based research framework that treats reviews, ratings, and brand response rates as primary inputs. He also covers persona-based query fan-outs as a replacement for single-prompt tracking, and documentation systems that let AI agents repeat classification at scale.

• Part 2Are you calling it SEO, AEO, or GEO?

  SEO, AEO, and GEO now compete as labels for the same enterprise discipline. Venkata Pagadala, AI Product Manager for Search at AT&T, leads agentic AI work for enterprise search. The conversation covers turning first-party data into deeper consumer understanding, applying agentic AI to enterprise SEO workflows, and treating search as the core discipline with AEO and GEO as facets of it.nn*Note: the transcript provided covers only the opening exchange. It has no statistics, results, or detailed frameworks, so I left them out rather than invent them. Share the full transcript if you want specific metrics and strategies added.*

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