Discover sources and result pages.
Request contextual search results when the agent needs candidate URLs, rankings, or engine features for a query.
- Query and engine context
- Market or language inputs where documented
- Result positions and result types
AI agent infrastructure
Agentic Web Access for current public web context.
Give your AI application a clear path to search, retrieve, crawl, navigate, and request structured web data. Your agent controls the workflow; WebScrapingAPI handles the access layer behind each API call.
- Search SERP API
- Retrieve Scraper API
- Crawl API
- Navigate Browser API
- Structure Data API
Route by task
Use the smallest web operation that completes the agent's next step.
Keep discovery, page access, multi-page collection, browser interaction, and structured extraction as explicit tools. The agent can choose a product from task intent instead of forcing every workload through one retrieval path.
Fetch a known public page.
Use a direct page request when the application already knows the URL and needs the response for parsing, summarization, or verification.
- Target URL and request context
- Access handling inside the service
- Response ready for your extraction logic
Collect a defined set of related pages.
Choose a crawl when one answer depends on a controlled page set rather than a single URL. Define the collection boundary before the application processes the results.
- Seed and scope defined by the workload
- Multi-page retrieval path
- Results passed to your pipeline
Reach the rendered state the task requires.
Use browser-backed access for client-rendered content or a documented interaction sequence. Your application sends the shortest explicit action plan that reaches the required state.
- Page-state waits
- Documented click, scroll, type, select, and submit actions
- HTML, structured response, or screenshot choices
Request a maintained source-specific record.
Use a supported Data API target when the agent needs normalized fields instead of building its own extraction model for that source.
- Source-specific request inputs
- Maintained extraction and normalization
- Structured records for downstream logic
Architecture boundary
Keep agent reasoning separate from web execution.
Your application decides why and when to use the web. WebScrapingAPI performs the selected public-web operation through the relevant product. The returned data then re-enters your application with the context needed for the next decision.
Plan the task.
The model and orchestration layer choose the goal, tool, request context, retry policy, memory, and acceptance rule.
- Prompts and model behavior
- Tool routing and task state
- Downstream decisions
Execute the selected web operation.
The relevant product handles its documented access, rendering, search, crawl, or structured-data behavior. Product-specific controls stay explicit and independently testable.
- Public-web access infrastructure
- Product request processing
- Maintained extraction for supported Data API targets
Return usable context.
Your application receives the selected response and can retain source, request, capture, and task context alongside its reasoning trace.
- Response and source context
- Application task identifier
- Acceptance or next-step state
Agent patterns
Compose focused workflows instead of one oversized web tool.
Start with a single high-value journey and make each transition observable. These patterns show how the products can complement one another without prescribing an agent framework.
Find, open, and retain the source.
Support an answer with current public evidence and a visible retrieval path.
- Search for candidate result pages
- Retrieve the selected source page
- Store source and capture context with the answer
Expand from a query to a bounded source set.
Move from discovery into a controlled collection when one page is not enough.
- Discover candidate domains and pages
- Define the crawl boundary
- Normalize results inside the research pipeline
Add maintained fields to an application record.
Use a supported source-specific API when the task needs repeatable fields rather than raw page content.
- Select the supported target and record type
- Request the required context
- Merge the returned fields under your validation rules
Integration
Give every web task an explicit contract.
Wrap each product call as a small tool in your application. Keep the task type, target, request context, expected response, and error handling visible so the agent can choose and evaluate the next step.
{
"task": "retrieve",
"product": "scraper_api",
"target": "selected_public_url",
"context": {
"task_id": "research_204",
"reason": "verify_source"
},
"retain": [
"source", "captured_at", "response"
]
}
- Use your application stack
- Call documented HTTP interfaces from the language, service, or orchestration layer that already runs your agent.
- Keep product controls intact
- Expose only the request options the task needs, then validate them before sending the API call.
- Normalize after retrieval
- Translate product responses into one internal result envelope without hiding source or request context.
Operating ownership
Know which layer your team operates.
The cleanest production boundary keeps agent behavior with your application and product operations with WebScrapingAPI. The exact extraction boundary depends on the product selected for each task.
Your team operates
The agent and its decisions.
- Model, prompts, planning, memory, and tool selection
- Request validation and application-level retries
- Result acceptance, citations, storage, and downstream actions
- Source approval and intended use
WebScrapingAPI operates
The selected web data product.
- Service availability and access infrastructure
- Documented search, retrieval, crawl, or browser behavior
- Maintained extraction for supported Data API targets
- Product response delivery to your application
Product map
Choose the product from the web object the agent needs.
Evaluate each path independently, then combine only the operations required by the production journey.
FAQ
Evaluate the web layer before expanding the agent.
Start with one task, one product, representative public sources, and a result contract your application can inspect.
What is Agentic Web Access?
Agentic Web Access is the public-web data layer for an AI application. Your application decides what task to run, then calls the relevant WebScrapingAPI product to search, retrieve a page, crawl a defined scope, interact with a dynamic page, or request a maintained structured record.
Which API should an agent call first?
Start with the task. Use SERP API for search results, Scraper API for a known page, Crawl API for a defined multi-page collection, Browser API for a rendered state or guided interaction, and Data API for a supported source-specific structured record.
Does WebScrapingAPI run the AI agent?
Your team runs the agent, model, planning logic, memory, and downstream decisions. WebScrapingAPI provides the public-web access and data products the application calls when it needs external information.
How can an agent work with dynamic websites?
Use Browser API when the required information appears after client-side rendering or a documented action such as clicking, scrolling, typing, selecting, submitting, or waiting for a page state. Keep the action sequence as short and explicit as the task allows.
Can results retain source evidence?
Design the result envelope to keep the source URL, request context, capture time, product response, and application-level task identifier together. Your application can then store or cite that context alongside the generated answer or decision.
Can the same architecture support scheduled updates?
Yes. Use the request-time APIs when the application chooses each task. For a recurring buyer-defined collection, use Data Feeds or Managed Data so WebScrapingAPI operates the schedule, extraction, maintenance, quality monitoring, and delivery.
How does an application connect to the products?
Call the products through their documented interfaces from your application or orchestration layer. The developer overview and integrations hub help teams evaluate language, framework, request, and deployment patterns without changing the agent architecture.
Is Agentic Web Access the same as AI training data?
No. Agentic Web Access supports request-time web tasks performed by an application. The AI training and grounding hub also covers prepared corpora, evaluation data, recurring RAG collections, video data, and managed delivery.
Your first agent journey
Turn one web-dependent task into an inspectable production workflow.
Bring the agent goal, representative sources, required context, expected result, and current application stack. We will map each web operation to the product that fits it.