Google published a research paper about creating a challenging dataset for training AI agents for deep research. The paper offers insights into how agentic AI deep research works, which implies insights for optimizing content.
The acronym SAGE stands for Steerable Agentic Data Generation for Deep Search with Execution Feedback.
Synthetic Question And Answer Pairs
The researchers noted that the previous state of the art AI training datasets (like Musique and HotpotQA) required no more than four reasoning steps in order to answer the questions. On the number of searches needed to answer a question, Musique averages 2.7 searches per question and HotpotQA averaged 2.1 searches. Another commonly used dataset named Natural Questions (NQ) only required an average of 1.3 searches per question.
These datasets that are used to train AI agents created a training gap for deep search tasks that required more reasoning steps and a greater number of searches. How can you train an AI agent for complex real-world deep search tasks if the AI agents haven’t been trained to tackle genuinely difficult questions.
The researchers created a system called SAGE that automatically generates high-quality, complex question-answer pairs for training AI search agents. SAGE is a “dual-agent” system where one AI writes a question and a second “search agent” AI tries to solve it, providing feedback on the complexity of the question.
- The goal of the first AI is to write a question that’s challenging to answer and requires many reasoning steps and multiple searches to solve.
- The goal of the second AI is try to measure if the question is answerable and calculate how difficult it is (minimum number of search steps required).
The key to SAGE is that if the second AI solves the question too easily or gets it wrong, the specific steps and documents it found (the execution trace) is fed back to the first AI. This feedback enables the first AI to identify one of four shortcuts that enable the second AI to solve the question in fewer steps.
It’s these shortcuts that provide insights into how to rank better for deep research tasks.
Four Ways That Deep Research Was Avoided
The goal of the paper was to create a set of question and answer pairs that were so difficult that it took the AI agent multiple steps to solve. The feedback showed four ways that made it less necessary for the AI agent to do additional searches to find an answer.
Four Reasons Deep Research Was Unnecessary
- Information Co-Location
This is the most common shortcut, accounting for 35% of the times when deep research was not necessary. This happens when two or more pieces of information needed to answer a question are located in the same document. Instead of searching twice, the AI finds both answers in one “hop”. - Multi-query Collapse
This happened in 21% of cases. The cause is when a single, clever search query retrieves enough information from different documents to solve multiple parts of the problem at once. This “collapses” what should have been a multi-step process into a single step. - Superficial Complexity
This accounts for 13% of times when deep research was not necessary. The question looks long and complicated to a human, but a search engine (that an AI agent is using) can jump straight to the answer without needing to reason through the intermediate steps. - Overly Specific Questions
31% of the failures are questions that contain so much detail that the answer becomes obvious in the very first search, removing the need for any “deep” investigation.
The researchers found that some questions look hard but are actually relatively easy because the information is “co-located” in one document. If an agent can answer a 4-hop question in 1 hop because one website was comprehensive enough to have all the answers, that data point is considered a failure for training the agent for reasoning but it’s still something that can happen in real-life and the agent will take advantage of finding all the information on one page.
SEO Takeaways
It’s possible to gain some insights into what kinds of content satisfies the deep research. While these aren’t necessarily tactics for ranking better in agentic AI deep search, these insights do show what kinds of scenarios caused the AI agents to find all or most of the answers in one web page.
“Information Co-location” Could Be An SEO Win
The researchers found that when multiple pieces of information required to answer a question occur in the same document, it reduces the number of search steps needed. For a publisher, this means consolidating “scattered” facts into one page prevents an AI agent from having to “hop” to a competitor’s site to find the rest of the answer.
Triggering “Multi-query Collapse”
The authors identified a phenomenon where information from different documents can be retrieved using a single query. By structuring content to answer several sub-questions at once, you enable the agent to find the full solution on your page faster, effectively “short-circuiting” the long reasoning chain the agent was prepared to undertake.
Eliminating “Shortcuts” (The Reasoning Gap)
The research paper notes that the data generator fails when it accidentally creates a “shortcut” to the answer. As an SEO, your goal is to be that shortcut—providing the specific data points like calculations, dates, or names that allow the agent to reach the final answer without further exploration.
The Goal Is Still To Rank In Classic Search
For an SEO and a publisher, these shortcuts underline the value of creating a comprehensive document because it will remove the need for an AI agent from getting triggered to hop somewhere else. This doesn’t mean it will be helpful to add all the information in one page. If it makes sense for a user it may be useful to link out from one page to another page for related information.
The reason I say that is because the AI agent is conducting classic search looking for answers, so the goal remains to optimize a web page for classic search. Furthermore, in this research, the AI agent is pulling from the top three ranked web pages for each query that it’s executing. I don’t know if this is how agentic AI search works in a live environment, but this is something to consider.
In fact, one of the tests that the researchers did was conducted using the Serper API to extract search results from Google.
So when it comes to ranking in agentic AI search, consider these takeaways:
- It may be useful to consider the importance of ranking in the top three.
- Do optimize web pages for classic search.
- Do not optimize web pages for AI search
- If it’s possible to be comprehensive, remain on-topic, and rank in the top three, then do that.
- Interlink to relevant pages to help those rank in classic search, preferably in the top three (to be safe).
It could be that agentic AI search will consider pulling from more than the top three in classic search. But it may be helpful to set the goal of ranking for the top 3 in classic search and to focus on ranking other pages that may be a part of the multi-hop deep research.
The research paper was published by Google on January 26, 2026. It’s available in PDF form: SAGE: Steerable Agentic Data Generation for Deep Search with Execution Feedback.
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