How do you implement AI agents that interact with web browsers and GUI applications?

Web and GUI agents automate tasks that require interaction with graphical interfaces. Web agents: use browser automation (Playwright, Puppeteer, Selenium) controlled by an LLM that observes page state, reasons about actions, and executes clicks, typing, and navigation. Implementation: capture page state (simplified DOM, accessibility tree, or screenshot), send to LLM with task instructions, parse action output (click element, type text, scroll, navigate), execute action via browser automation, and repeat. Observation methods: DOM-based (extract structured representation of page elements with their attributes and text), screenshot-based (send page screenshot to a vision model that identifies interactive elements), and accessibility tree (use ARIA attributes for a compact, semantically rich page representation). Frameworks: Playwright MCP for browser control, Browser Use (Python framework for LLM-controlled browser automation), and Anthropic's computer use capability. GUI agents: extend beyond browsers to desktop applications using screen capture and accessibility APIs. Implement action primitives (click at coordinates, type text, scroll, keyboard shortcuts) and use vision models to understand application state. Challenges: dynamic page content (handle loading states, animations, pop-ups), authentication flows (manage cookies and sessions), anti-bot protection (implement ethical scraping practices), and reliability (pages change layout frequently, breaking hardcoded selectors). Best practices: use semantic selectors (ARIA labels, test IDs) rather than CSS selectors, implement retry logic for flaky interactions, set timeouts for page loads, and validate action outcomes (verify the expected state after each action). Security: sandbox browser execution, restrict network access, implement URL allowlists, and prevent credential storage in agent memory.