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To make a chat agent search the web and cite its sources, give it a search tool, a rule for deciding whether another search is needed, and a citation-aware answer format. A useful agent does not search repeatedly by default: it searches again when the first results leave a specific question unanswered, reveal a conflict, or fail to support an important claim.
What multi-pass search means
A multi-pass search agent works through a task in stages: it interprets the request, retrieves relevant sources, inspects the results, identifies remaining gaps, and searches again only when those gaps matter. It then writes an answer that connects material factual claims to the sources that support them.
This is a workflow, not a guarantee of accuracy. More searches can add useful evidence, but they can also return redundant or conflicting material. The agent needs a reason to continue, and the final answer still needs evidence checks.
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Choose the right search mode for the task
OpenAI describes three approaches: non-reasoning web search, agentic search with reasoning, and Deep Research. Its documentation positions agentic search for complex workflows in which a model can analyze results and decide whether to continue searching. Deep Research is designed for multi-step or in-depth questions that combine sources into a more structured report; ordinary search is faster for quick facts. Availability of Deep Research varies by plan and country or territory. OpenAI web search documentation and the Deep Research Help Center article describe these distinctions.
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| Approach | Best fit | What to expect |
|---|---|---|
| Fast or non-reasoning search | A quick, well-defined fact lookup | Retrieves current information without treating the task as an extended investigation. |
| Reasoning-managed or progressive search | A question with unresolved subquestions, source gaps, or evidence that may change the next query | The model can inspect earlier results and decide whether a targeted follow-up search is useful. |
| Deep research | A multi-step investigation that needs multiple sources synthesized into a report | Designed for depth and thoroughness rather than a quick answer; access depends on plan and location. |
These are workflow distinctions, not a neutral ranking. The cited provider documentation does not establish comparative accuracy, latency, or cost.
How to decide whether the agent should search again
Use each follow-up search to answer a named question. After the initial retrieval, the agent should inspect what the sources establish and what remains uncertain. Search again when a missing detail is necessary to answer the user, an important claim lacks adequate support, or credible sources disagree in a way that affects the conclusion.
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- Retrieve: Search for the core question and collect source material relevant to the answer.
- Inspect: Check each result’s relevance and what it actually supports; do not treat a search snippet as proof of the whole page’s contents.
- Identify a gap: State the unanswered material question, unsupported claim, or conflict in concrete terms.
- Refine the query: Search for that gap rather than repeating the broad initial query. Where appropriate, narrow by topic, date, jurisdiction, or domain.
- Stop when sufficient: When the material questions are supported and no unresolved conflict changes the answer, synthesize the evidence instead of continuing to search.
Anthropic documents progressive searches in which earlier results inform later queries, along with query refinement and domain allow/block controls. Its guidance describes a way to conduct light research and produce a more comprehensive answer, not a promise that every extra search improves correctness. See the Claude web-search documentation.
Where to implement search and citation controls
The integration surface determines how search is invoked and what evidence the model receives. OpenAI documents web search in the Responses API and search modes in its Agents API. Anthropic documents web search and citation-capable supplied search results for the Claude Messages API. Choose based on the controls your application needs, not on an assumed provider-wide accuracy advantage.
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| Integration | Documented controls or behavior | Citation information |
|---|---|---|
| OpenAI Responses API web search | Provides web search for up-to-date information; agentic search can be reasoning-managed. | Responses can include URL citation annotations with URL, title, and location. OpenAI web search documentation |
| OpenAI Agents API web search | Documents live, cached, and disabled search modes, plus optional context-size and domain settings. | Consult the API documentation for the response and citation details relevant to the selected setup. OpenAI web search documentation |
| Claude Messages API web search | Documents progressive searching, query refinement, and domain allow/block controls. | Search responses include source URL, title, and cited text. Anthropic web-search documentation |
| Claude Messages API with supplied search results | Your application supplies search-result content to the model. | Result blocks can include a source identifier and title; citations can be enabled so the answer cites supplied results. Anthropic citations documentation |
Before implementation, verify the current API reference for the chosen integration: provider APIs and plan access can change. Compare the controls you actually require, including whether the model chooses when to search, live versus cached access, allowed calls or progressive searching, domain restrictions, localization, and the citation metadata available to your application.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make citations support the claims they accompany
A list of links at the end of an answer is less useful than citations attached to the claims those sources support. Design the answer step so it preserves the connection between retrieved evidence and each material factual statement. OpenAI documents sourced web-search citations and URL citation annotations; Anthropic documents citation data in web-search responses and citations for supplied search results.
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- Attach a citation near the specific claim it supports, rather than citing a source that only addresses the topic generally.
- Use the returned source metadata, such as URL and title, to make citations identifiable and actionable.
- Do not let the model imply that a source supports a detail it does not contain. If evidence is incomplete or conflicting, qualify the answer.
- For supplied search results, retain source identifiers and enable citation support so the response can point back to the supplied evidence.
Citation formatting helps readers verify claims; it does not itself establish that a source is authoritative or that the claim has been interpreted correctly. Evaluation should therefore check whether important claims are supported, not merely whether the answer contains links.
Quick Recap
A practical design checklist
- Choose fast search for quick facts, reasoning-managed or progressive search when results may reveal important follow-up questions, and a deeper research workflow for multi-step synthesis.
- Define what counts as a material gap or source conflict before allowing another search.
- Set appropriate domain, context-size, localization, and search-mode controls where the integration exposes them.
- Preserve source metadata through generation so citations can be attached to the claims they support.
- Check the current provider documentation and access terms for the API and region you intend to use.
- Do not treat search-call count, citation presence, or a polished report as a substitute for checking evidence quality.
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