How AI Agents Get Information from the Web: Search, Crawling, and Structured Extraction

The names Tavily, Exa, Firecrawl, Crawl4AI, Jina Reader, Nimble, GPT Researcher, Open Deep Research, are listed in a row, and can easily be written into tool encyclopedia. A subsection on each tool, describing functionality, prices, applicable scenes. You can read a bunch of terms, but you still don't know which floor you're going to take.

This article can also be read alongside How I Build RAG: From Project Experience to a Systematic Approach to see how related ideas develop in different contexts.

I would like to ask another question: how does AI Agent get the information available on the Internet?

For humans, the Internet is usually a browser, a search box, a web page and a link. For Agent, it is a long information conduit: first, to identify possible sources, then to open the page, to extract the text, to remove navigation and advertising, to convert dynamic pages or PDF into modelable text, and then to sift out duplicate, obsolete and conflicting segments, and then to enter the context with references. Any part of the middle is broken and the following is simply a copy of the material.

So instead of being presented by the manufacturer, the article looks down the line. Search, web-page capture and structured cleansing are not the same issues. But they're all connected to Agent's access to information from the Internet.

Overall:

Phase	Representative Tool	Main card points	Changes relative to previous period	The problem that still exists.
Model-inline network	OpenAI Web Search、Claude Web Search、Gemini Grounding、xAI Web Search / X Search	Let the model directly check real-time information and give the source during the generation	The fastest access, the developers don't have to build their own search and reading links.	Lack of transparency in the search process, selection of sources, intermediate evidence and retry strategy
Trust Web Retrieval	Tavily, Exa, and traditional search/SERP API	Pack search and initial reading into a callable API	Developer can control query, domain name, time, number of results and output patterns	The material is already in hand, not to say that the organization is reliable.
Web Page Parsing, Capture and Structured Cleaning	Firecrawl、Crawl4AI、Jina Reader、Trafilatura	Convert URLs, sites and documents to data that can be accessed into context or knowledge base	From returned link to readable, ubiquitous, extractable material	Dynamic pages, tables, metadata, weighting and version governance still to be processed
Deep Research becomes a long-range workflow	Open Deep Research、GPT Researcher、STORM、Perplexica	Organize search, reading, synthesis and reference into a multistep research cycle	Tool call is no longer a search, but query planning, multi-hop search, cross-check	The difficulty of assessing, the fact that the correct reference is not reliable, the report seems to be quite realistic.
Commercial capture platform	Nimble、Bright Data、Oxylabs	Hand over high-wind control, regionalization, dynamic pages, SERP, electricians and social media collection to specialized platforms	From "Pages Can Parsing" to "data that can stabilize access to target scenes"	Still decide what to pick, how to enter, how to access RAG, Agent or the analysis system

Phase 1: Models built-in network, fastest and blackest

The most economical approach is to use the networking tools provided directly by model manufacturers. OpenAI Web SearchAnthropic. Claude web search toolGemini's. Grounding with Google Searchin xAI tool documents Web Search and X SearchIt's all in this floor.

This route is less complicated. You don't have to decide on your own search engine, you don't write reptiles, you don't clean pages, you don't use design references. The model needs to search for new information, get results, and return the answers and references together. This is sufficient for many general questions and answers, simple information queries and news background.

Trouble is here: the search process has turned us into a black box.

The developers can see the final answer and some of the quotes, but they may not see it in its entirety why it searched for this query, why it chose these sources, why it abandoned other sources, or whether it missed important pages. Networking capacity is then moving from a tool call to a strategy within the model. OpenAI's Reponse API further magnifies this point, and the API requested by the user is no longer a model but a set of services, and the user is not entitled to know the details within the service.

This is a double-edged sword for the developers.

If you're just asking if this product was released today, it's okay to be black. But if you're doing financial, legal, medical, business intelligence, competitive intelligence, or any system that requires back-linking evidence, the black box will get tougher. You need to know what the system has searched, what it hasn't searched, where every reference comes from, whether the same conclusion is supported by a second source. The model's built-in network can give you answers, but it's not necessarily credible.

So the first stage is the question of whether the model can access new information. It does not address the ability of developers to manage this access to information.

Phase 2: hosting Web Retrieval, packing search and initial reading

The second stage was not simply to remove the search from the model or to return to the traditional search results page. More precisely, it packs "search + initial content capture" into a hosting API.

Tavily and Exa are very close. They are trying to turn the search results into materials that the model can continue to consume: receive or generate query, find the relevant web pages, return answers, abstracts, text, references or highlights. The real difference is in the degree of containment and control.

Tavily More like a one-stop-shop. It places the search, extract, crawl, Map and cid research in the same product context, which is suitable for quickly connecting Agent to external web-based information. You can understand it as a few less lines: first get the readable results and then let the model continue to reason.

Exa More like a controlled semantic search and content acquisition interface. Its Search API allows both the contents, the document and the changelog to emphasize Markdown content, fresh control (use) maxAgeHours The ability to update pages, domain flying, highlights, etc. It leaves more knobs for developers: source range, content format, freshness, text.

Search result interfaces such as Brave Search API, SerpApi, Serper can also be used as a complement to source discovery layers, but the SERP ecology is not developed here. Most Agent is not going to be able to use these, and it is enough to use more mature services.

Up to this level, the developers no longer get a set of URLs or snippet. The hosting search API has made a part of your reading and sorting and has kept some controllable freedom.

But it still cannot replace a specialized dialysis layer. Read and wash back into a separate engineering layer if you are to build a knowledge base, make a replicable assessment, process a large number of URLs, or require the body, table, metadata to be authentic.

Phase III: Web-based analysis, capture and structured cleansing

At this level, the question is how to go back from searching for API to any URL, document station and file that stabilizes into the context of the model.

The better the hosting search API, the easier it is to think that the page resolution is no longer a problem. Not really. The page is readable for the person, but the model is often a noise: navigation bar, recommendation bar, cookie bullet window, ad, CSS class, hydration data, comment area, hidden nodes, scripts, duplicate footers. Single page reading, site capture, structured extraction appear to be three functions, placed in RAG and Agent, but in fact a chain.

Firecrawl just stood in the middle of the chain.Scrape (b) Converting individual URLs into formats such as Markdown, HTML, JSON, screenshot, etc., and also handle non-web content such as PDF;Crawl (a) Responsible for the retrieving of the site and processing of sitemap, JavaScript rendering, path filtering and depth limits; Map used to discover site structures;Extract , select one or more URLs, or even the whole domain, as structured fields by prompt or schema. In this way, Firecrawl is not simply reader, nor crawler, but rather helping developers to keep less of a grab and cleaning pot.

This is where it fits Agent and RAG: you want less agency, more reggae, more PDF, more sites, more dirty work, and more clean input materials. Firecrawl can take this floor of basic work. And what's the heavy, how's the chunk, how's the source gonna be credible, how's the results going to be written into the knowledge base, or your system design?

Other tools could be complementary.Jina Reader Fits to temporary URL reading, simple, fast, but without site-level governance.Trafilatura It is a practical text extraction tool for the construction of Python text extracts pipe lines, a good engineering tool before the emergence of the generation AI.Crawl4AI It is appropriate for teams that want to keep capture capacity locally: to control and distribute, cache, page selection and deployment, and to bear the cost of failure recovery and maintenance.

Read is not a subsidiary to the search. Search only tells you where there may be answers. The resolution and capture determine whether the materials are clean enough to enter the context. Many RAG systems appear to be retrieving, but the reading phase is already broken: the body is not drawn, the table is missing, the old and new versions are mixed, the table of contents is used as the content page, and the PDF is cut into a bunch of contextless pieces.

These questions are not as bright as modeling, but they determine that the foundations of the RAG and research anent are unstable.

Phase 4: Deep Research transforms search into a long-range workflow

Moving up, the search is no longer a single search, but a research process.

OpenAI documents describe Deep Research as an individual-driven input for longer periods. The blogger says:Open Deep Research、GPT Researcher、STORM、Perplexica(Renamed Vane) is doing something close: giving a problem, self-dismantling the system, searching multiple sources, reading web pages, collating evidence, generating reports and, to the extent possible, bringing references.

The system handles things that put a search in a loop:

问题
  -> 拆成子问题
  -> 生成搜索 query
  -> 找来源
  -> 读取和抽取页面
  -> 判断证据是否足够
  -> 继续搜索或修正方向
  -> 综合报告
  -> 引用和反查

This layer exposes a new problem: the reference is not as credible as it is credible.

A search agent can link each passage, but the link may support only half of it. It may also cite the correct pages, but it misreads them; or it finds many sources, but they copy each other. Even more problematic is the fact that long reports are naturally reliable. The paragraph is complete, quoted, and the tone is calm, and the guard is relaxed.

So benchmark starts to follow. OpenAI BrowseComp (a) A permanent search capability using 1,266 hard-to-reach but verifiable short-answer browsers;GAIA General AI views on the performance of hybrid missions such as tool use, web pages, reasoning, etc.DeepResearch Bench and DeepResearchGym The report, evidence and replicability of the paper are more directly seen.

These assessments have their own limitations. Short answers are not the same as true research, and LLM-as-judge is not the final decision. But they remind us at least that what is really difficult is to find, to look again and again, to know what is missing and to align the evidence and conclusions.

Phase V: commercial capture platformization

The normal page resolution is to give me a URL, can I read it out? In the future, the challenge will become whether the system can continue to have access to available data on high-wind control, high-scale, high-area-discretion websites. The problem is not just about HTML washing, but about proxy networks, browser rendering, counter-blocking, targeting station fit, batching and delivery.

This is where the Nimbale, Bright Data, Oxylabs, such commercial capture platforms appear. Rather than making models more summarised, they plaster some of the most easily towed systems in web-based data collection: proxy and regionalization visits, JavaScript rendering, SERP collection, electrical page analysis, social media, maps and local search data, bulk tasking, and structured output.

Nimble More like hosting collector layers for real-time web data. It provides Web, SERP, E-commerce, Maps and others API, which seals agents, regionalized access, browser rendering, counter-blocking and data delivery. For Agent or RAG systems, Nimble solves the structured facts behind steady access to certain types of pages.

Bright Data The focus is more complete data acquisition infrastructure: proxy networks, Web Scraper API, SERP API, pre-set scraper, data sets and structured outputs. It is suitable for tasks such as price monitoring, catalogues of electricians, search results, open web-based data sets, and places anti-robots, authentication code processing, target station fit and JSON/CSV delivery to the side of the platform as far as possible.Oxylabs Similar positions are being held, but more emphasis is placed on enterprise-level proxy networks and large-scale public web data collection.

These three platforms work together to help us not understand the web pages, but to keep web data available. After the agent, unseal, rendering, decomposition, bulk collection, target station suitability and delivery are handed over to mature platforms, the application side is more concerned with what data is needed, how often updates, and which fields and evidence are to be retained before entering the RAG, Agent or the analysis system.

Two real main lines: what to see, what to control.

After reading these tools, the problem can be harvested into two lines.

The first is what Agent saw.

The model is built into a network that allows it to see the final search results and references. Hosting the API to see candidate sources and pre-packaged materials. The parse and grab layer allows it to see the text, site structure and structured fields. Deep Research showed it a list of intermediate evidence. The commercial capture platform allows it to see more steadily the target data in the SERP, electrician, social media, map and regionalization pages.

Information is not as simple as entering a model from a web page. It is rewritten on every level: SERP snippet, Markdown, chunk, JSON Schema, Summary, Citation, report. Every rewriting, there is an opportunity for loss and misunderstanding.

The second is what developers can control.

The model contains the least networked but less cost-effective. Hosting the API API allows you to control query, source and part of the content. The parse tool allows you to control the page reading and cleaning. The grab frame allows you to control the site. Structured extraction allows you to control fields. Deep Research framework allows you to control the research cycle. Commercial capture platforms make you more stable external capacity for agency, area, target station and data delivery.

This is the question that should be asked when choosing a model: Which floor do you want to give to the model and which floor must be in your hands?

The tool selection should go back to the level of your card.

If you lack the latest information, but the task is light, you can start with a model of network. OpenAI, Claude, Gemini, Grok, these capabilities are fast enough and less.

If you want to get a consumer web file quickly, see Tavily and Exa. Tavily is more like a one-stop shop, Exa is more suitable for a controlled semantic search and for contensions. Traditional search/SERP API could be complemented by the fact that they do not mistakenly consider that the web-page reading and evidence organization has been resolved.

If you find the material but it's not clean, see Firecrawl, Jina Reader, Trafilatura. Don't rush to change models. The website is being properly deciphered, cleaned and broken.

If you lack station-level capture, RG language construction and structured cleaning, see Firecrawl Crawl, Map, Extract, or Crawl4AI, this self-custody route. The problem here is data engineering, searching is just the entrance.

If you lack long-range research, see Open Deep Research, GPT Researcher, STORM, Perplexica, with the evaluation. Deep Research, which is not evaluated, can easily become a pretty report generator.

If you lack high-wind control sites, SERP, electricians, social media, price surveillance or regionalized access, see Nimble, Bright Data, Oxylabs. The focus here is not to make the model smarter, but to stabilize the system to obtain target data.

Concluding remarks

The Internet enters Agent, not a web-based feed to the model.

It is more like a fragmented engineering link: search for sources, read the text, wash for noise reduction, structure for fields, research loops for evidence supplementation, commercial capture platforms for high-wind control and localized web data stabilization.

The stronger the model, the clearer the link. Because models make bad material sound like real. The results of the search were broken and people can still look at themselves; now Agent reads, sums up, writes for others. It's misread while the error is packaged.

Okay, Agent Information Access System, not all pages are plugged into models. We need better results, and we need to make it more worthy of human belief.