Learn how AI browser agents work behind the scenes using the agent loop: plan, act, observe, and repeat. In this lecture, we break down how an LLM connects with browser automation tools like Playwright to read web pages, click buttons, type into forms, extract data, and complete multi-step browser tasks.

In this lecture, we introduce the core tools used throughout the course, including Python, Playwright, LLMs, Streamlit, Docker, and AWS. You will understand why each tool matters and how they work together to build real-world agentic AI browser automation systems.

Discover why AI browser agents are becoming important for developers, automation engineers, and AI builders. This lecture explains practical use cases such as web research, product comparison, data extraction, form filling, workflow automation, and human-in-the-loop AI systems.

Learn the basics of the DOM API and how browser automation tools understand web pages. In this lecture, we cover HTML structure, elements, buttons, inputs, forms, links, and selectors so you can confidently locate and interact with page elements using Playwright.

Set up the project from scratch and launch your first automated browser using Python and Playwright. This lecture walks through the initial environment setup, dependency installation, browser launch, and the difference between headless and headful browser automation.

Learn how to read content from web pages and extract useful information using Playwright. In this lecture, we cover how to capture headings, paragraphs, product details, links, prices, ratings, and other structured data from websites.

Understand the three levels of browser agents: basic scripted automation, LLM-assisted browser workflows, and fully autonomous browser agents. This lecture helps you clearly see the difference between normal automation and true agentic AI browser behavior.

Build a practical AI shopping research agent that can search the web, analyze product results, and extract useful shopping information. This lecture shows how browser automation and LLM reasoning can work together to compare products, prices, ratings, and summaries.

Learn how to build a fully autonomous browser agent loop where the AI can plan, take browser actions, observe results, and decide the next step. This lecture introduces the core architecture behind agentic AI systems that can operate across multiple steps with guardrails.

Build a safer AI browser agent by adding human review and approval before final actions are submitted. This lecture explains why human-in-the-loop design is important for browser automation, especially for forms, approvals, sensitive workflows, and real-world business use cases.

Learn how to upload CSV files into the AI browser agent system so multiple requests can be processed in a structured workflow. This lecture introduces file upload handling, batch processing concepts, and how uploaded data moves into the automation pipeline.

Convert uploaded CSV data into approval requests that can be reviewed, tracked, and processed by the agent system. This lecture covers how to read uploaded records, create structured requests, manage statuses, and prepare the system for human approval workflows.

Deploy the backend of the AI browser agent system using AWS services such as S3, SQS, Lambda, and DynamoDB. This lecture explains the cloud architecture and shows how uploaded files can trigger backend processing in a serverless workflow.

Deploy the Streamlit user interface for the AI browser agent system using Docker and AWS Lightsail Containers. This lecture shows how to package the Playwright-based application, connect it with the backend, and run the browser agent UI in the cloud.

Understand the ethics and safety concerns of building AI browser agents. This lecture covers responsible automation, CAPTCHA handling, terms of service, rate limits, prompt injection, human oversight, and safe deployment practices for real-world agentic AI browser systems.