AI agents are becoming increasingly capable of reasoning, planning, analyzing information, and executing multi-step tasks.
But there is one important challenge: How does an AI agent actually interact with the real web?
An LLM can generate a search query, understand instructions, summarize information, and decide what to do next. However, interacting with a modern website often requires more than generating text.
It may require opening a browser, navigating to a URL, waiting for JavaScript to load, clicking buttons, filling out forms, scrolling through pages, extracting information, and handling dynamic website content.
This is where browser infrastructure becomes an important part of AI agent development.
Instead of maintaining browser environments on their own servers, developers can use Cloud Browser Infrastructure (Browser-as-a-Service) to give their applications and AI agents access to remotely hosted browsers.
Let's explore why AI agents need browsers, how cloud browser infrastructure works, and how developers can use Agently to build browser-enabled AI applications.
An AI agent is not simply a language model answering questions.
A typical agent workflow involves several stages:
For many applications, these steps require access to external websites.
Consider an AI research assistant.
A user might ask:
"Research five project management platforms, compare their features, and summarize their pricing pages."
An LLM can reason about the task and organize the information. But it still needs a way to access those websites and retrieve their current content.
A browser provides that interaction layer.
The agent can use a browser to:
The distinction is important:
The AI agent provides the reasoning. The browser provides the environment for web interaction.
Without an appropriate execution environment, browser-dependent tasks become difficult to implement reliably.
A cloud browser is a remotely hosted browser environment that applications can access over a network.
Instead of installing Chromium on your application server, configuring browser dependencies, and managing browser processes yourself, you connect to a browser running on external infrastructure.
This approach is commonly associated with Browser-as-a-Service (BaaS).
For AI agent developers, a cloud browser can serve as the execution environment through which an agent interacts with websites.
A typical architecture looks like this:
User Request → AI Agent → Browser Automation Layer → Cloud Browser → Website
The process works as follows:
This separates AI reasoning from browser execution.
It also allows developers to focus on their application logic instead of building and maintaining all the browser infrastructure themselves.
Building an AI agent that can interact with websites is one challenge. Operating the browser infrastructure behind it is another.
When developers build browser automation systems using locally hosted Chromium, they may need to manage:
Browser installation and configuration
Chromium and its dependencies must be installed and maintained in the execution environment.
Resource management
Browser processes consume memory and CPU resources. Multiple concurrent sessions require appropriate infrastructure planning.
Browser lifecycle management
Applications need to handle browser startup, connections, session termination, and unexpected failures.
Dynamic website rendering
Modern websites frequently depend on JavaScript frameworks and client-side rendering. Static HTML extraction may not provide the content an agent needs.
Scaling automation workloads
As the number of browser-dependent tasks increases, managing browser instances can become an additional engineering responsibility.
These challenges can take time away from developing the actual AI product.
This is why managed cloud browser infrastructure is worth considering.
Agently is a cloud-based Browser-as-a-Service platform designed for developers, AI agents, automation systems, and LLM applications.
Its central purpose is to provide hosted Chromium browsers and web extraction tools without requiring developers to install, host, configure, and maintain their own browser infrastructure.
One of its main capabilities is the Live Browser Sandbox (Cloud Chromium).
Developers can connect to remotely hosted browser sessions through WebSocket connections and Chrome DevTools Protocol (CDP).
Agently also supports browser automation workflows using Playwright and Puppeteer.
This means developers can use familiar browser automation tools while connecting to remotely hosted browser infrastructure.
For example, a Python application can connect to an Agently-hosted Chromium browser using Playwright:
from playwright.async_api import async_playwright
async def run():
async with async_playwright() as p:
browser = await p.chromium.connect_over_cdp(
"wss://agently.jobdriver.org/v1/connect?apiKey=YOUR_API_KEY"
)
page = await browser.new_page()
await page.goto("https://example.com")
print(await page.title())
await browser.close()
Replace YOUR_API_KEY with your actual API key.
In this example:
The important part is that the browser does not need to run locally on the application's own server.
Developers can build on their existing Playwright workflows while using a managed browser environment.
Once an AI application has access to a browser, developers can build a variety of browser-enabled workflows.
Here are some practical examples.
An AI research agent can visit multiple websites, collect relevant information, and pass the extracted content to an LLM for analysis.
A possible workflow:
This is useful for research assistants, competitive intelligence applications, and knowledge discovery tools.
Some tasks require interacting with a website rather than simply reading its content.
For example, an agent might need to navigate through multiple pages, interact with forms, or click interface elements.
With a remote browser connected through Playwright or Puppeteer, developers can implement these browser actions as part of their agent workflows.
The agent decides what action is needed, while the automation layer executes it.
An application could visit product pages, extract publicly available product information, and collect data such as:
This information can then be processed by an LLM or stored in an application's database.
AI-powered applications may also need to inspect how websites look or change over time.
Browser infrastructure can support workflows involving:
AI applications that use Retrieval-Augmented Generation (RAG) need relevant external information in a format that can be processed and indexed.
A website can be crawled, its content extracted, converted into Markdown, and prepared for embedding generation and vector database storage.
This creates a practical connection between browser infrastructure, web extraction, and LLM-powered knowledge systems.
A cloud browser provides an execution environment, but many AI workflows also need clean and structured web content.
Extracting an entire HTML document is not always the most useful approach for an LLM.
Web pages contain navigation elements, scripts, advertisements, formatting, and other information that may not contribute to the task.
Agently combines cloud browser infrastructure with web extraction tools designed to prepare website information for applications.
Its advertised tools include:
URL to Markdown
Converts website content into clean Markdown that can be used in LLM prompts, RAG pipelines, and knowledge-processing workflows.
Article Extractor
Extracts the main content of articles and blog posts, including available title, author, and publication information.
Plain Text Extractor
Converts webpage content into readable text without unnecessary HTML formatting.
Scrape to JSON
Extracts selected website information into structured JSON using CSS selectors and supported structured data.
JavaScript HTML Renderer
Renders JavaScript-powered websites before extracting their rendered HTML and DOM content.
Website Crawler
Discovers and explores website pages recursively, supporting website indexing and content collection workflows.
These capabilities are useful because AI applications often need both website interaction and content extraction.
Rather than treating browser automation and web data extraction as completely separate problems, developers can use them as complementary parts of their architecture.
Let's put the components together.
Imagine building an AI agent that researches competitors' websites.
A possible architecture would look like this:
Layer 1: User Interface
The user submits a research request.
Layer 2: AI Agent
The LLM interprets the request, identifies the required websites, and determines what information to collect.
Layer 3: Agent Tool and Automation Layer
The application translates the agent's decisions into browser actions using an automation framework such as Playwright.
Layer 4: Cloud Browser Infrastructure
Agently provides the remote Chromium browser through a CDP/WebSocket connection.
Layer 5: Website and Data Extraction
The browser accesses websites, while extraction tools can help convert relevant information into readable Markdown, plain text, or structured JSON.
Layer 6: AI Processing
The collected information is passed back to the LLM for comparison, summarization, or structured analysis.
The resulting architecture separates responsibilities:
This separation makes the system easier to reason about and gives developers flexibility when designing their AI workflows.
A cloud browser is particularly worth considering when an application:
For a small script that visits one static page occasionally, local browser automation might be sufficient.
However, when browser execution becomes a recurring infrastructure requirement, a managed cloud browser can simplify the architecture and reduce the amount of browser infrastructure developers need to maintain.
The right choice depends on workload, concurrency, reliability requirements, security, and cost.
AI agents are becoming more useful when they can interact with external tools and real-world information.
But building an effective browser-enabled AI application requires more than an LLM.
It requires a way to access websites, execute browser actions, render dynamic pages, and retrieve information in formats that AI systems can process.
That is the problem cloud browser infrastructure is designed to address.
Agently brings together remote Chromium browser sessions, Playwright and Puppeteer connectivity, and web extraction capabilities in one developer-focused platform.
Instead of spending all your engineering effort on browser installation and infrastructure management, you can focus on the agent logic and application workflows you want to build.
Building an AI agent that needs to interact with the web?
Explore Agently and start experimenting with cloud browser infrastructure:
Explore the developer documentation:
https://agently.jobdriver.org/docs
Explore the available tools and APIs:
https://agently.jobdriver.org/tools
A cloud browser for AI agents is a remotely hosted browser environment that allows AI applications to access websites, navigate pages, interact with web elements, and retrieve information without hosting the browser themselves.
AI agents need browser automation when their tasks involve interacting with websites, accessing dynamic content, navigating pages, filling forms, or collecting information that is not directly available through an API.
Browser-as-a-Service is a model in which developers access remotely hosted browser infrastructure instead of installing and maintaining browser instances on their own servers.
Yes. Playwright supports connecting to remote Chromium browser instances through Chrome DevTools Protocol using connect_over_cdp(), provided the browser service exposes a compatible connection endpoint.
An AI agent handles reasoning, planning, and decision-making. A cloud browser provides the environment in which browser-based actions are executed. They serve different but complementary roles.
Yes. A cloud browser can help access and render web content, while extraction tools can convert that content into formats such as Markdown or structured text for subsequent indexing and RAG processing.
Agently is a cloud-based Browser-as-a-Service and web extraction platform that provides remote Chromium browser sessions, browser automation connectivity, and APIs for extracting and processing website content.
Final Thought
The next generation of AI applications will need more than the ability to generate text. Many will need to access websites, understand dynamic information, and interact with web interfaces.
A cloud browser provides an important piece of that architecture.
For developers building web-aware AI agents, browser infrastructure deserves to be treated as a core part of the application stack—not an afterthought.