Chrome is outdated, AI Agent needs its own browser.

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A few days ago, I saw the news that genspark's Super Agent reached tens of millions of dollars in ARR, breaking the fastest record of AI products. AI Agents are being recognized by everyone. Today, I will talk to you about AI native browsers in the AI ​​Agent era. You may have noticed that whether it is OpenAI, Google, or Manus, they are all giving AI Agents the ability to browse and operate web pages. To complete tasks, Agents often cannot do without the Internet, a huge source of information and interactive interface.

But here comes the problem. The browsers we use now, such as Chrome and Safari, are designed for us humans. They have beautiful graphical interfaces, suitable for us to click with the mouse. But what about AI Agents? Most of them run on cloud servers, without monitors, and do not need to "see" web pages. Letting Agents use a tool optimized for human vision is a bit "unsuitable". This is like asking a program that only knows how to interact with code to operate a complex graphics software. Not only is it inefficient, but it may also run into obstacles everywhere.

So why do AI agents need their own specially designed browser?

Traditional browsers are not suitable for large AI models

Traditional browsers are optimized for human use on local computers, while AI Agents are usually deployed on cloud servers and need to be run programmatically and on a large scale. Running hundreds or thousands of browser instances optimized for graphical interfaces directly on the server has huge performance overhead and is extremely complex to manage.

Secondly, the core of the design of these browsers is user experience (UX), which is full of various paradigms designed for human interaction, such as buttons, menus, and visual layouts. AI Agent controls these elements through code, which is far less efficient and stable than directly calling APIs.

The challenges of traditional automation

Some people may say, aren't there headless browser libraries like Puppeteer and Playwright? They allow us to control the browser with code, perform automation and data scraping. This is indeed the mainstream way at present, but there are various problems in practice.

There are generally three common problems:

Dynamic loading. Today's web pages use a lot of JavaScript to dynamically load content. The HTML you get with a simple HTTP request is often incomplete. You must simulate a complete browser environment and run the page script to get the real data. A lot of data only appears gradually after the initial loading.

Interaction issues. Often, the data we need is not directly visible, but requires clicking a button, filling out a form, or scrolling the page to obtain it. For example, if you want to crawl an article, you are blocked by a pop-up window that requires you to enter an email address. This requires writing complex automation scripts to simulate human operations.

Anti-crawling. In order to prevent being crawled by robots, websites have deployed various anti-crawling mechanisms. The most common one is CAPTCHA, and there are more advanced browser fingerprint recognition, user behavior analysis, etc. Developers need to use various techniques to disguise requests, such as using proxy IP and simulating real browser header information. The process is cumbersome and the success rate cannot be guaranteed.

Even if you bypass anti-crawl, selectors are still a headache. Currently, the main way to control browsers is CSS selectors, which are used to locate elements on the page. However, these selectors are very fragile. If the front-end code of the web page is slightly changed (for example, the developer adjusts the div structure or class name), the previously written script will immediately crash. Maintaining these scripts requires a lot of effort.

Finally, in order to provide comprehensive browser functions, some automation libraries package a lot of things that AI Agents may not even use, which may result in huge installation packages and affect cloud deployment.

Special demands of AI Agent: Automation is not enough

The above mentioned are only the problems of traditional automation. When the protagonist is replaced by AI Agent, the requirements are raised to a new level.

AI agents are not just robots that execute preset scripts. They need to have a certain degree of autonomous decision-making ability . For example, on a shopping website, the agent needs to judge which pages to search, how to filter products, and what information to parse based on the user's vague instructions ("Help me find a cost-effective Bluetooth headset"). This requires the agent to understand the context and structure of the web page, rather than just relying on hard-coded CSS selectors.

They also need to adapt to the dynamically changing network environment. Website interfaces and content change frequently. Traditional RPA (robotic process automation) scripts need to be manually updated in this case, while the ideal AI Agent should be able to adapt to these changes more intelligently, just like humans, and be able to find the button when it is moved to a different location. Recently, there was also a discussion on Reddit about the adaptability advantage of AI browser automation over RPA.

In addition, many application scenarios require large-scale concurrency. Imagine that an AI customer service system may need to handle web query requests from hundreds or thousands of users at the same time. This requires the underlying browser infrastructure to be highly scalable and able to instantly start and manage a large number of browser sessions. Browserbase particularly emphasizes their ability to start thousands of browsers within milliseconds.

More direct demands come from large models. Whether it is to let LLM acquire the latest knowledge through RAG (retrieval enhanced generation) or to let LLM autonomously perform network tasks through Plugins/Web Agents, a stable, efficient and easy-to-integrate browser interface is required. In his "LLM operating system", Andrej Karpathy listed the browser, file system and vector database as core basic components. This is enough to show the importance of the browser in bringing out the capabilities of LLM.

“AI-native” browser

Faced with the limitations of traditional browsers and the new demands of AI Agents, the industry is calling for a new browser solution that is "born for AI". Are there any startups that are doing well in this regard? Yes, Paul Klein, the founder of Browserbase, saw this very early, and he talked about it in an early interview.

What should this next generation solution look like?

First of all, it needs to be a highly optimized, lightweight, cloud-native infrastructure. Say goodbye to bloated dependencies and complex deployment, be able to easily scale, and support large-scale concurrency. This is exactly what companies like Browserbase are working towards. They provide cloud-based headless browser services, allowing developers to focus on AI logic rather than browser operations.

Secondly, and most importantly, AI is used to give browsers "superpowers." Instead of relying on fragile CSS selectors, LLM is used to understand the structure and content of web pages, and even VLM (visual language model) is used to directly "understand" page screenshots.

Developers can interact with the browser in a more natural way, such as directly giving instructions: "Find price information" or "Click the red login button." The Stagehand framework launched by Browserbase is an attempt to drive browser operations using natural language instructions.

At the same time, more intelligent information retrieval capabilities are also needed. AI Agents must not only be able to "act" on web pages, but also "find" information efficiently. Search engines designed specifically for AI, such as Exa, provide more accurate results than traditional keyword searches through semantic understanding, and can better meet the needs of Agents for research, analysis and other tasks.

Finally, all of this needs to be provided through a new, developer-friendly interface (SDK/API). The interface design needs to be more adapted to the working mode of AI Agents, such as better handling asynchronous operations, retries, and complex logic branches.

at last

The importance of AI Agent dedicated browsers is not only judged by a few start-ups, but also confirmed by industry giants.

OpenAI has launched Operator, which has browsing capabilities and runs on a remote Chromium instance. Google has also been revealed to be developing Project Mariner, an AI system that can control browsers to complete online tasks. Even browser manufacturer Opera has built-in AI Agents in its browser that can perform web page tasks.

These actions clearly show that enabling AI Agents to interact with the network seamlessly and efficiently has become an important direction for AI development.

Of course, building a perfect AI Agent browser is not an easy task. How to continuously and effectively fight against anti-crawler mechanisms? How to ensure security and prevent Agents from being maliciously exploited? How to deal with data privacy and ethical issues? How to improve the reliability and accuracy of Agent operations? These are challenges that need to be continuously explored and solved.

But the direction is clear: the future of AI agents is inseparable from their ability to effectively browse and interact with the Internet. Traditional browsers obviously cannot fully meet this need.

So, back to the original question: Why does an AI Agent need its own browser?

Because they need to run on a large scale in the cloud, need to be efficiently controlled by code, need to understand web content and make autonomous decisions, need to adapt to dynamic changes, and need to be seamlessly integrated with LLM. All of these are difficult for traditional browsers designed for humans and their automation tools to perfectly provide.

A browser infrastructure designed for AI and powered by AI is becoming the key to unleashing the full potential of AI Agents. Pioneers like Browserbase and Exa are building this future for us.