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An agent that clicks through websites has to read both screenshots and page structure, and a single wrong click can compound into an unexpected outcome. The course frames this problem before building anything, covering how web agents are architected and where they currently fall short.
A substantial section covers AgentQ, which combines tree search, self-critique and a preference-optimization method so agents can improve from their own mistakes. A Gridworld animation makes the search process concrete.
Learners with basic Python who want to make browser agents more robust will find the hands-on work well matched to that goal.
Learn how to build AI agents that interact with websites in Building AI Browser Agents, taught by Div Garg and Naman Garg, Co-founders of AGI Inc, and built in partnership with AGI Inc.
AI browser agents can log into websites, fill out forms, click through web pages, or even place an online order for you. They use both visual information, like screenshots, and structural data, like the HTML or Document Object Model (DOM) of a web page, to reason and take actions.
With the complexity of web pages and many possible actions at each step, it can be challenging for an AI browser agent to complete an assigned task. A single error—like clicking the wrong button or misreading a field—can compound into unexpected outcomes.
In this course, you’ll understand how autonomous web agents work, their current limitations, and how AgentQ enables them to improve through self-correction.
In detail, you’ll:
By the end of this course, you will have hands-on experience building browser agents and a deeper understanding of how to make them more robust and reliable.
Who should join?
This course is ideal for learners with basic Python skills who want to explore how to build autonomous agents that interact with the web.
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