AI browser agents are one of the most practical use cases of agentic AI. Instead of only chatting with an AI model, you will learn how to build agents that can open a browser, read web pages, click buttons, type into forms, extract data, and complete multi-step workflows.

In this course, we will build everything from scratch using Python, Playwright, LLMs, Streamlit, Docker, and AWS. You will begin with the fundamentals of browser automation, including how web pages are structured, how DOM selectors work, how to interact with buttons and forms, and how to extract useful information from websites.

Then we will add LLM intelligence so the agent can understand user instructions, plan browser actions, make decisions, and continue working through an automation flow. You will build practical projects including an AI shopping research agent, a fully autonomous browser agent loop, and a human-in-the-loop approval workflow.

You will also learn how to upload files, process records, create approval requests, track statuses, handle errors, and build a simple Streamlit interface for managing the agent system. In the deployment section, we will move the project to the cloud using AWS services such as S3, SQS, Lambda, DynamoDB, Docker, and Lightsail Containers.

We will also cover important safety and ethics topics such as CAPTCHA handling, prompt injection, terms of service, rate limits, and responsible browser automation.

No prior experience with Playwright, browser agents, Streamlit, Docker, or AWS is required. We will build the project step by step from the ground up. By the end, you will have a complete project you can show in your portfolio, extend for your own workflows, and use as a foundation for building more advanced AI automation systems.