AI Agent with Browser Automation

Abstract

This project presents the design and implementation of an intelligent AI agent capable of performing automated actions within a web browser environment. The proposed system integrates natural-language understanding, task decomposition, and browser-level automation to execute user- defined goals such as data extraction, form submission, website navigation, report generation, and repetitive workflow operations. The agent combines machine learning models with rule-based logic to accurately interpret user instructions, convert them into executable steps, and interact with web elements in real time. To ensure robustness, a lightweight automation framework is incorporated to manage element detection, handle dynamic page layouts, and recover from unexpected interface changes or errors. The system is further enhanced with decision-making capabilities that allow the agent to adapt its actions based on webpage behavior, user constraints, and context awareness. Experimental evaluation demonstrates that the AI agent significantly reduces manual effort, improves operational accuracy, and accelerates digital processes when compared to conventional browser automation tools or static scripts. Overall, this work highlights the growing potential of AI- driven autonomous agents in modern web environments and establishes a practical foundation for future advancements in self-guided, multi-step browser task execution across various domains.