How does agentic search affect keyword strategy?

Agentic search does not mean abandoning keywords. It means broadening research beyond individual terms to the questions users want answered, the tasks they want completed, and related subquestions. According to the supplied material, when an agent uses “query fan-out,” a single prompt may lead to multiple subqueries covering synonyms, related entities, and different intents. When selecting keywords, consider not only whether a term matches the original wording, but also whether the content fully answers this set of questions. However, a substantial shift from information searches toward agent-led tasks remains a prediction, not an established pattern of user behavior.

Expand keyword selection from individual queries to tasks and subquestions

Semrush describes agentic search as both gathering information to answer questions and completing tasks such as shopping on users’ behalf. Keyword research therefore needs to go beyond identifying topics to mapping out the questions users may ask before comparing options, making choices, and taking action.

For example, starting with “leather handbags,” topics to investigate could include “material differences,” “capacity needs,” and “care methods.” These are only examples for topic planning: the supplied material does not establish search volumes for these questions, and an agent will not necessarily generate these queries. The point is that if a system uses query fan-out, content that simply repeats the original terms may not adequately cover related subquestions.

At Procrustes, we turn the questions our clients genuinely care about into articles, with ongoing content planning, writing, and updates.

Traditional keyword tools are a starting point, not a measure of how often AI prompts are used

The Semrush article treats Keyword Planner as a starting point for researching SEO keywords and agentic search prompts. The tool’s related terms, average monthly search volumes, and trends can help build an initial list. However, the supplied material does not establish that these figures reflect how often users enter prompts into AI tools.

• Use related terms to guide research: First group related questions, then research how your audience asks AI questions. Do not treat tool suggestions as validated demand.
• Understand what the metrics mean: “Competition” in the article refers to advertiser bidding activity. It cannot be used to determine how difficult it is for content to be cited by AI.
• Consider the list’s limits: The article notes that suggested terms tend to stay close to the seed terms. Do not assume a list from a single tool covers everything users need to complete their tasks.

Use keywords to identify section topics rather than repeating them excessively

Semrush recommends clearly identifying the topic at the start of each section and answering the question fully and accurately within it. Use brand, product, and feature names consistently, rather than relying solely on references such as “it” or “this tool.” These recommendations are intended to help systems recognize and cite content. They are not official search-platform rules, and following them does not guarantee inclusion.

After selecting keywords, check whether each paragraph provides a sufficient answer to a clear question, rather than simply counting how often a term appears. Clear writing does not mean removing necessary qualifications: applicable contexts, exceptions, and uncertainty still need to be retained.

When adjusting your strategy, also distinguish predictions from changes that have already happened. Search Engine Journal explicitly cautions that Pichai’s comments about long-running, asynchronous agent tasks represent Google’s predictions about the future. These predictions can guide research, but they do not justify completely discarding existing keyword strategies.