Google published a research paper on developing a challenging dataset to train AI agents for deep research tasks, providing insights into how agentic AI deep research operates and implications for content optimization. The paper introduces SAGE, which stands for Steerable Agentic Data Generation for Deep Search with Execution Feedback—a “dual-agent” system that generates complex question-answer pairs for training AI search agents. Prior datasets such as Musique, HotpotQA, and Natural Questions (NQ) involved relatively few reasoning steps (average searches per question: Musique 2. 7, HotpotQA 2. 1, NQ 1. 3), leaving a training gap for AI agents needing to handle genuinely difficult, multi-step deep search queries. SAGE addresses this by having one AI write challenging questions requiring multiple reasoning steps and searches, while a second “search agent” attempts to answer them, providing feedback on question difficulty and execution (search steps and documents used). When the second AI solves questions too easily or fails, execution traces feed back to the first AI, helping it identify shortcuts that reduce reasoning complexity. The researchers identified four main shortcuts that allowed AI agents to avoid deep research: 1. **Information Co-Location (35%)**: Key information pieces needed to answer a question are found in the same document, enabling fewer searches. 2. **Multi-query Collapse (21%)**: A single, effective search query retrieves sufficient information from different documents in one step. 3. **Superficial Complexity (13%)**: Questions appear complex but allow direct answers via search without intermediate reasoning. 4.

**Overly Specific Questions (31%)**: Highly detailed questions lead to answers on the first search, eliminating the need for deep investigation. These shortcuts help explain how AI agents reduce deep reasoning steps and offer SEO-relevant insights. For publishers, consolidating scattered facts ("Information Co-location") into one comprehensive page can reduce AI’s need to "hop" to competitor sites. Structuring content to answer multiple sub-questions simultaneously ("Multi-query Collapse") helps AI agents find full solutions faster, effectively shortening the reasoning chain. Providing specific data points (dates, calculations, names) can act as shortcuts that allow AI to reach answers quickly, which aligns with SEO goals. Despite these agentic AI insights, the paper stresses that the primary SEO focus should remain on ranking well in classic search, as the AI agents in the study pull results from the top three ranked pages per query, based on Google’s Serper API. Thus, content creators should: - Optimize web pages primarily for classic search. - Aim to be comprehensive and on-topic while ranking in the top three results. - Interlink to related pages to help them also rank well and potentially support multi-hop deep research. - Avoid focusing solely on AI search optimization, given current reliance on classic search rankings in agentic AI systems. In conclusion, while agentic AI deep search presents new challenges, SEO strategies that emphasize comprehensive, well-structured content optimized for classic search remain effective. The research, published by Google on January 26, 2026, is available as the paper “SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback. ”