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Foundations of AI Agents for Web Automation

Course Overview and Success Criteria

Agent Architecture and Planning for the Web

Tools and Integrations for Browser Control

Designing End-to-End Agentic Workflows

Navigation, Authentication, and Session Management

Data Extraction and Validation

Form Filling, Transactions, and Multi-Step Decisions

Orchestration and Reliability Engineering

Security, Ethics, and Compliance for Web Automation

Responsible Automation Practices

Capstone: Build and Ship an AI Web Automation Agent

Project Blueprint and Implementation

Knowledge Checks and Feedback

Quiz: Agent Architecture and Planning

Quiz: Browser Control and DOM Interaction

Quiz: Navigation, Auth, and Sessions

Quiz: Data Extraction and Validation

Quiz: Orchestration and Reliability

Course Feedback Survey

Master the end-to-end design of AI agent workflows for reliable web automation. In this hands-on course, you’ll architect, build, and ship agentic systems that can navigate complex websites, authenticate securely, extract and validate data at scale, fill forms and execute transactions, and recover gracefully from errors. You’ll learn the core loop of modern agents—perceive the DOM and page state, plan with constraints, act via structured browser tools, and reflect to improve decisions—while applying best practices for state, memory, and observability. We’ll compare Playwright, Puppeteer, Selenium, and CDP, and show how to combine DOM-first strategies with vision models for brittle UIs. You’ll design resilient selectors, handle pagination and infinite scroll, and export clean data to CSV, JSONL, databases, and vector stores. Finally, you’ll orchestrate production-grade workflows with timeouts, circuit breakers, logging, traces, screenshots, and chaos tests. The capstone guides you from blueprint to deployment, with guardrails, human-in-the-loop approvals, and rollback strategies. If you’re ready to move beyond demos and ship real, reliable AI web agents, this course is your playbook.

AI Agent Web Automation: Designing End-to-End Workflow Orchestration

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            <p>AI Agent Web Automation: Designing End-to-End Workflow Orchestration equips you to build production-grade agents that navigate, extract, and act across real websites with reliability and control. You’ll start by grounding in what AI agents are and why they matter for the modern web, mapping success criteria to concrete workflows like scraping, navigation, form handling, and decision-making. The course clarifies the skills and tools you’ll need—Python or JavaScript, browser automation frameworks, APIs, LLMs, and headless browsers—while helping you choose a local or cloud environment with a clear privacy and security posture.</p>

<p>From there, you’ll learn to architect intelligent agents specifically for the web. We’ll break down agent roles (planner, executor, reviewer, memory manager) and operationalize a robust core loop: perceive the DOM and UI state, plan, act, and reflect. You’ll practice prompting patterns such as toolformer prompts and action schemas with explicit constraints and guardrails, select appropriate models (LLMs, vision models for rendered UIs, and embeddings for memory), and implement state and memory strategies for multi-step tasks.</p>

<p>The course then dives into the mechanics of reliable browser control. You’ll work with Playwright/Puppeteer, Selenium, and the Chrome DevTools Protocol, define structured tool interfaces (click, type, select, wait, scroll, upload, download), and master DOM access via selectors, ARIA roles, XPath, and accessibility trees. Special attention is paid to dynamic front-ends—SPA routing, iframes, virtual lists, and lazy loading—and vision-plus-DOM techniques for brittle UIs.</p>

<p>With foundations in place, you’ll design end-to-end agentic workflows. Learn multi-page navigation and breadcrumb planning, handle authentication patterns (passwords, MFA, OAuth) and CAPTCHA policies, manage session state and anti-bot considerations, and implement error recovery with retries, backoff, and human-in-the-loop escalation. For data work, you’ll create resilient selector strategies, manage pagination and infinite scroll, parse diverse structures (tables, lists, cards, JSON endpoints), and enforce schema validation, deduplication, and exports to CSV, JSONL, databases, or vector stores.</p>

<p>Action flows receive equal rigor: locate inputs and constraints, overcome client-side validation, and orchestrate complex tasks like carts, checkouts, bookings, nested dialogs, and file uploads. You’ll define agent decision policies with goal decomposition, guardrails, and approval steps, and engineer idempotency, confirmation detection, and rollback strategies to keep transactions safe. On the orchestration front, you’ll compare options such as LangGraph, OpenAI Assistants, and custom state machines; set tool-use limits, timeouts, and circuit breakers; and build observability with logs, traces, screenshots, DOM snapshots, and replays. Testing practices span fixtures, mock sites, and chaos tests, while scaling topics cover concurrency, queues, and cost control.</p>

<p>Throughout, you’ll internalize responsible automation practices: respecting robots.txt and terms of service, managing PII and credentials securely, maintaining audit trails and human oversight for sensitive actions, and applying fair-use and anti-abuse principles. The capstone pulls everything together as you blueprint, build, and ship a real AI web automation agent—defining a target workflow with clear constraints and success metrics; designing roles, tools, memory, and guardrails; delivering increments across navigation, extraction, forms, and decisions; and preparing a deployment runbook with monitoring, rollback, and operational handoff. Knowledge checks and a feedback survey help you validate mastery and continuously refine your approach to building dependable, ethical, and scalable web automation agents.</p>
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