Most SEO teams can still confirm whether their content is ranking.
Far fewer can answer the question that matters now: of the 10 pieces published last month, how many were indexed, how many were cited in ChatGPT, and how many still held their position by week three.
That measurement gap is the new AI search challenge. And it is widening as more AI engines enter the mix.
Why Cross-Engine Tracking Has Outgrown The Old Playbook
Citations are now distributed across ChatGPT, Claude, Perplexity, AI Overviews, AI Mode, and several other engines. Each one indexes content differently. Each one cites sources differently. And the supporting data sits in six to twelve tools that do not integrate with one another.
Dashboards can identify the problem. They cannot resolve it. SEO professionals end up handling the data consolidation, prioritization, and follow-up manually, which is the part of the work that does not scale.
A Case Study From Building An AI Agent System For Cross-Engine Search
In an upcoming Search Engine Journal webinar, Sam Garg, Founder and CEO of Writesonic, will share what his team learned from building a system of AI agents to manage the cross-engine workflow: identifying citation gaps, prioritizing fixes, drafting updates for review, and verifying the change held after publication.
Here is what attendees will take away:
- The four-layer framework behind a working marketing agent: identity, knowledge, skills, and loops. Most AI tools stop at layer two.
- Five lessons from running agents alongside a marketing team in production, including how an organization’s structure shapes the agents that succeed.
- A walkthrough of a citation-outreach system that surfaces opportunities and drafts outreach by 7 AM, with open-source components available for teams that want to build their own version.
About The Speaker
Sam Garg is the founder and CEO of Writesonic, where his team has deployed AI agents into the marketing workflows many SEO teams are still managing manually. He will share the working code, the actual results, and the parts of the project that did not perform as expected.
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