This process is known as query rewriting. It allows the agent to act as a more effective intermediary between a human request and the structure of online content.
The original query is treated as a starting point, not a final instruction.
Why Rewrite a Query?
Natural language queries are often unsuited for direct use with search engines. They may be:
- Too vague, such as "privacy risks"
- Overly narrow, like "best GDPR guide for fintech apps under 20 pages"
- Ambiguous, including phrases like "safe to invest" without context
Rewriting improves the likelihood of retrieving information that matches the user's intent.
How Agents Rewrite Queries
1. Clarifying Ambiguity
An AI agent can detect when a query lacks clarity and restructure it for greater precision. For example, a request such as "Will it rain soon?" may become "7-day weather forecast for Newcastle NSW."
This removes ambiguity and ensures a better match with available data.
2. Expanding with Synonyms and Boolean Variants
Agents often substitute or add synonyms to increase the search scope. A query about "low energy homes" might be rewritten to include terms such as "energy-efficient housing," "passive houses," or "green building designs."
In some cases, agents construct Boolean-style combinations. This involves multiple rewritten queries or a single query that includes OR-based variants, such as:
"energy-efficient" OR "eco-friendly" OR "green" AND "homes"
Although Boolean operators are not always used explicitly in modern search engines, the logical structure remains. The agent simulates this behaviour to surface more comprehensive results.
3. Personalizing with User Context
Agents with access to contextual information about the user may incorporate that data into the rewritten query. This includes:
- Location: adjusting "weather today" to "current weather in Hobart"
- Past interactions: building on earlier questions or known interests
- Preferences: changing "holiday options" to "quiet holiday destinations with hiking"
This contextualization is not fixed, but dynamic based on session data or profile inputs.
4. Drawing on Internal Knowledge
Large Language Models (LLMs) can apply pre-existing knowledge when shaping search queries. If a user types "AI models for text," the agent might infer this refers to tools like GPT-4, Claude 3, or Gemini, and generate:
"Compare GPT-4.1 vs Claude 4 for text generation accuracy"
This internal reference base helps align the rewritten query with what is known to exist on the web.
5. Inferring Intent
Agents use language models to infer why a user is asking a question. This inferred intent often differs from the literal wording. For example:
- "VPN issues" could become "how to fix VPN not connecting on Windows 11"
- "flight options" might be reframed as "cheapest international flights from Brisbane in July"
This process is based on understanding action types such as learning, comparing, locating, or troubleshooting.
6. Generating Multiple Queries
Rather than selecting a single rephrasing, agents may produce multiple rewritten queries to cover different angles. These variations may differ in:
- Level of specificity
- Use of alternative terminology
- Inclusion or exclusion of context
Each query is evaluated individually, and results can be compared, ranked, or even combined. This approach mimics the behaviour of a skilled human researcher who reformulates a search several times to get to the right information.
It is not uncommon for an agent to run several queries in parallel, each expressing a slightly different understanding of the original request.
A Changing Relationship Between Queries and Results
As AI agents increasingly mediate access to online information, the form and function of queries continue to evolve. The relationship between what a user types and what is searched becomes more layered - involving inference, adaptation, and logic. While users may never see these intermediate rewrites, they shape every result an agent provides.
The rewritten query, not the original prompt, is often what the search engine actually sees.
This hidden step is central to how agents interact with the web, and explains why their answers can differ in both style and substance from those produced by direct human search.
