Course Includes:
- Price: FREE
- Enrolled: 310 students
- Language: English
- Certificate: Yes
- Difficulty: Beginner
Coupon verified 06:59 PM (updated every 10 min)
Course Folder
What You'll Learn
- Build agentic AI browser agents that can browse websites
- click buttons
- type into forms
- extract information
- and complete multi-step web tasks.,Use Python and Playwright to control a real browser and automate common web workflows.,Connect LLMs to browser automation so an AI agent can understand user instructions
- plan actions
- and make decisions during a workflow.,Extract structured data from websites and save results into clean formats such as tables and CSV files.,Build practical approval-based workflows where AI assists with form filling but keeps a human in the loop before final submission.,Create a Streamlit user interface for uploading files
- running browser agents
- reviewing results
- and tracking status.,Understand safety
- ethics
- and limitations of browser AI agents
- including CAPTCHA handling
- prompt injection
- and responsible automation.,Deploy an agentic browser automation project using Docker and AWS services such as S3
- SQS
- Lambda
- DynamoDB
- and Lightsail Containers.
Requirements
- No prior experience with browser automation is required,No prior experience with Playwright is required,No prior experience with AI agents is required,No advanced AI or machine learning background is required,Basic Python knowledge is helpful
- but we will explain the code step by step,Basic understanding of websites
- buttons
- forms
- and web pages is helpful,A code editor such as Visual Studio Code is recommended,An AWS account is optional and only needed for the deployment section,Everything will be built from scratch
- step by step
- including Python setup
- Playwright basics
- AI agent logic
- UI
- Docker
- and deployment,An OpenAI API key or access to another LLM provider is recommended for the AI agent examples
Description
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.
Who this course is for:
- Beginner Python developers who want to build hands-on AI automation projects,Students who want to understand how AI agents can browse websites
- extract data
- and complete tasks,Automation engineers who want to add LLM-powered decision making to browser workflows,AI enthusiasts who want to move beyond simple chatbots and build agents that can take actions,Freelancers and builders who want to create practical automation tools for web tasks,Professionals who want to understand how agentic AI can be used for real-world business workflows,Learners who want a step-by-step project-based course instead of only theory
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