Disclaimer: This post isn’t our usual security-focused content – today we’re taking a quick detour to explore the fascinating world of AI agents with the focus of AI web agents. Enjoy this educational dive as a warm-up before we get into the juicy details of AI web agents in our follow-up post where we will Uncover Security Risks in AI Web Agents.

Introduction

Artificial Intelligence has evolved far beyond simple chatbots. Today’s AI agents are dynamic systems that can plan, interact with digital tools, and execute tasks with minimal human intervention. Unlike traditional applications, these agents can autonomously gather information, make decisions and take actions to achieve their goals. In this post, we’ll define what an AI agent is, with a special focus on AI web agents. We’ll also explore their core capabilities and show how they fit into modern multi‑agent systems. This foundational guide will equip you with the essential knowledge needed to appreciate the fast-evolving landscape of agentic AI and set the stage for our next deep dive into AI web agent vulnerabilities. Let’s dive in!

What is an AI Agent?

Before we can focus on AI web agents, let’s first understand what an AI agent is.

In simple terms, an AI agent is a software system that can autonomously perform tasks for a user or another system. Unlike a regular chatbot that only responses to inputs, an AI agent can make decisions, call APIs or databases, control software, and generally act in an environment to achieve a goal. These agents often leverage advanced large language models (LLMs) for understanding instructions and reasoning, but crucially they are not limited to their training data – they can reach out to tools and data sources to get things done.

Think of an AI agent as a tireless digital helper: you give it an objective, and it figures out the steps, finds the information or tools needed, and executes actions step by step (with minimal or no human intervention). It can remember context (with an internal memory) and adjust its plan on the fly.

What are AI Web Agents?

Now let’s turn our attention to the main topic: AI Web Agents. These agents are built specifically to interact with the World Wide Web. In simple terms, an AI web agent is an AI-powered system that can browse websites, understand web content, and perform actions inside a web browser, just like a human would, but entirely on its own.

In the context of our earlier discussion, a web agent is essentially an AI agent whose environment is the web. Instead of relying only on internal data, it perceives information on web pages (via HTML, text, and sometimes visuals), and can click links, fill forms, or trigger other web-based actions via a browser interface.

Behind the scenes, web agents often utilize a headless browser or APIs to fetch web pages, process their content (using natural language understanding or even computer vision to grasp layouts), and interact with the web elements. In doing so, they translate messy, human-oriented web interfaces into structured information that AI models can reason about and act upon, effectively making the web LLM-friendly.

Core Capabilities

AI web agents are powered by a set of essential skills. Below, we’ll break down each one and demonstrate how it works in real‑world scenarios.

1. Web Navigation

At the most basic level, a web agent must be able to move through the internet just like a human using a browser. This includes:

• Clicking links to explore menus, follow search results, or drill down into subpages.
• Filling out forms with text inputs, dropdowns, radio buttons, and checkboxes- whether it’s logging into a portal, submitting a search, or registering for an event.
• Handling dialogs like cookie consents or pop‑ups, allowing the agent to continue navigating without stumbling over unexpected prompts.

Example: An invoice‑download bot logs into your vendor portal, navigates to the billing page, selects last month’s date range, and clicks “Download PDF”.

2. Data Retrieval

Once the Agent reaches its target page, it needs to pull the precise information you’re looking for. This Includes:

• Scraping HTML to parse page structure and extract tables, lists, or headlines, even when the layout shifts unexpectedly.
• Calling JSON APIs to retrieve structured data (like stock prices or weather forecasts) and process the responses.
• Normalizing content by cleaning and reformatting text (stripping ads, collapsing whitespace) or converting image‑based charts into usable data.

Example: A daily briefing agent fetches the front pages of three tech blogs, scrapes the top five headlines and summaries from each, and consolidates them into a single daily email.

3. Task Execution

Beyond reading, AI agents can take meaningful action on your behalf:

• Posting content to social platforms, internal wikis, or CMS dashboards.
• Sending messages via email (SMTP), Slack/GitHub bots, or other communication channels.
• Triggering workflows in external systems (like launching a CI/CD pipeline, creating a Jira ticket, or starting a data‑backup job).

Example: After analyzing incoming customer feedback, an agent automatically drafts and sends personalized “thank you” emails to anyone who gave a 5‑star rating.

4. Workflow Chaining

The real magic happens when you link individual steps into a seamless pipeline:

• Detecting triggers by monitoring for new spreadsheet rows, incoming emails, or scheduled times.
• Gathering data through authentication, web navigation, scraping, or APIs calls.
• Processing information by summarizing text, performing calculations, and applying business logic.
• Acting on results by posting reports, updating dashboards, or sending notifications to stakeholders.
• Looping or branching based on outcomes: retry on failures, escalate errors, or split into parallel sub‑tasks.

Example: A “sales ops” agent watches your CRM for new leads, scrapes LinkedIn profiles for additional context, scores each lead via a simple formula, then creates a follow‑up task in your project management tool.

By mastering these four core capabilities, AI web agents can automate virtually any routine web‑based workflow, freeing you to focus on strategy, creativity, and problem‑solving. In the next section, we’ll explore the tools and architectures that make this possible.

AI Web Agents Implementations

AI web agents have 2 popular implementations you might encounter in the wild:

• Browser Automation Frameworks: These frameworks can navigate websites, click buttons, fill forms, and scrape content autonomously, like we just mentioned in the core capabilities. These frameworks provide the low-level browser hooks agents need to interact with virtually any page element.
• Desktop & Integrated AI Systems: These frameworks use features that merge web and local automation. Agents built on these platforms can manipulate both web content and native applications, allowing them to glance at your screen, open files, move windows, and perform hybrid tasks that span the browser and desktop environment.

AI Web Agents Frameworks

Instead of building every component from scratch, modern frameworks and services can handle the heavy lifting and accelerate agent development. Below are notable frameworks and services categorized by the two aforementioned implementation types:

Browser Automation Frameworks

• Browser‑Use is an open‑source toolkit that combines a headless browser (Playwright) with an LLM interface into a single, unified API. It offers built‑in actions for navigating pages, filling forms, clicking buttons, and scraping content, plus utilities for managing session state and capturing screenshots.
• Skyvern is an open-source AI agent platform designed to automate browser-based workflows using LLMs and computer vision. It replaces brittle scripts or manual processes with an AI that can handle web tasks on many different sites. Skyvern provides a simple API endpoint where you can describe a task, and it will execute it through a browser.

To illustrate these capabilities in action, here’s a demo where Browser-Use automates a Skyscanner search to find the cheapest flights from Belfast to London.

Demo 1: Automating Skyscanner Searches via Browser-Use

In the demo video Browser-Use performs the following steps:

1. Navigate to https://www.skyscanner.net
2. Fill the “From” field with Belfast and the “To” field with London
3. Select departure and return dates
4. Click the search button and wait for the results page to load
5. Scrape each flight’s price, airline name and departure time
6. Compare all prices and identify the cheapest flight option
7. Return a summary containing airline, price, departure time and a direct booking link

This simple end-to-end example shows how Browser-Use can handle complex page interactions, dynamic content loading and data extraction—all with a few high-level commands that mirror what a human user would do in a browser.

Desktop & Integrated AI Systems

• OpenAI’s Operator is a service that integrates LLM intelligence with both web browser and desktop automation. It can navigate websites, edit and send documents through native applications, run local scripts and interact with operating system functions using natural language prompts.
• Claude’s Computer Use is an extension of Anthropic’s Claude designed for hybrid web and desktop workflows. It can click through native application menus, adjust system settings, open files and browse the web with full desktop context while leveraging safety filters to catch risky commands.

Both Browser-Use and Skyvern highlight that AI web agents are no longer futuristic ideas and they’re accessible today. Browser-Use lowers the barrier for connecting an AI’s thought processes to real-world browser actions, offering cloud services and an open-source library, while Skyvern tackles the challenge of variability by giving agents eyes through computer vision. On the desktop side, OpenAI’s Operator and Claude’s Computer Use demonstrate that hybrid web and local automation is likewise within reach, enabling agents to navigate your system as easily as they browse the web. Taken together, these implementations and frameworks put powerful automation tools at your fingertips – and they underscore the importance of building robust security measures to prevent malicious uses of agentic capabilities.

Conclusion

To wrap up, AI web agents greatly expand the reach of agentic AI systems, by unlocking the door to the internet’s information and services. They transform the web into an extended memory and action space for AI. When combined with other specialized agents (for coding, math, interacting with local systems, etc.), they form a powerful ensemble that can autonomously tackle complex, open-ended tasks.

For general tech readers, the takeaway is simple: AI agents are no longer confined to answering questions, they can now take meaningful actions. Nowhere is this more evident than on the web. As this technology matures, we can expect AI assistants to do more and more: comparing products across sites and automatically ordering the best one, or performing an online task that we logged as a reminder to do later. It’s an exciting moment where the line between a human browsing the web and an AI doing it for us is starting to blur. The agentic AI landscape, with web agents as a key component, promises more automation, efficiency, and connectivity in our digital lives, ushering in a future where “going online to get something done” might just mean telling your AI agent and letting it handle the rest.

However, these powerful capabilities also open new attack vectors and security concerns, such as prompt injection, unauthorized automation and data leakage, which we will explore in depth in our follow-up blog.

Click here to continue reading about agentic AI risks in our next post!

The post The Rise of Agentic AI: From Chatbots to Web Agents appeared first on Blog.

*** This is a Security Bloggers Network syndicated blog from Blog authored by Sarit Yerushalmi. Read the original post at: https://www.imperva.com/blog/the-rise-of-agentic-ai-from-chatbots-to-web-agents/