Day 3 | How to Build Vision AI Agents: Screenshots, VLMs & Browser Control

Build a vision AI agent in Python that can see a webpage, take screenshots and act on what it observes. This is Course #3 of the Build AI Agents series — we move from text-only agents to agents that use vision language models (VLMs) to understand images and control a real browser. You'll download and process images with Python, feed them to a vision model, then automate Chrome with Selenium and Helium so the agent can find elements, close pop-ups, scroll, take screenshots and remember what it saw between steps. WHAT YOU'LL LEARN • What a vision agent is and why VLMs matter • Downloading and processing images with requests • Sending images to a vision model from a Python agent • Browser automation with Selenium and Helium • Finding page elements by text and handling missing ones • Screenshots, timing delays and converting PNG output • Agent memory: observations carried between steps • Giving an agent tools to click, scroll and navigate • What to do when the agent fails (and how to work around it) Prerequisites: Courses #1 and #2 — the agent loop and tool calling are already covered there. CODE https://github.com/KNehe/ai_agents FULL COURSE (playlist)    • BUILD AI AGENTS WITH PYTHON   OpenAI Billing https://platform.openai.com/settings/... Can my machine run this LLM? https://runthisllm.com/